Visit the National Academies Press online, the authoritative source for all books from the 

National Academy of Sciences, the National Academy of Engineering, the Institute of

Medicine, and the National Research Council: 

• Download hundreds of free books in PDF 

• Read thousands of books online, free 

• Sign up to be notified when new books are published 

• Purchase printed books 

• Purchase PDFs 

• Explore with our innovative research tools 

Thank you for downloading this free PDF. If you have comments, questions or just want 

more information about the books published by the National Academies Press, you may 

contact our customer service department toll-free at 888-624-8373, visit us online, or 

send an email to comments@nap.edu. 

This free book plus thousands more books are available at http://www.nap.edu.

Copyright © National Academy of Sciences. Permission is granted for this material to be 

shared for noncommercial, educational purposes, provided that this notice appears on the 

reproduced materials, the Web address of the online, full authoritative version is retained, 

and copies are not altered. To disseminate otherwise or to republish requires written 

permission from the National Academies Press. 

ISBN: 0-309-07408-8, 255 pages, 6 x 9, (2001)

This free PDF was downloaded from:

http://www.nap.edu/catalog/10080.html

Classifying Drinking Water Contaminants for 

Regulatory Consideration 

Committee on Drinking Water Contaminants, Water 

Science and Technology Board, Board on 

Environmental Studies and Toxicology, National 

Research Council 
Classifying Drinking

Water Contaminants

for Regulatory

Consideration

Committee on Drinking Water Contaminants

Water Science and Technology Board

Board on Environmental Studies and Toxicology

Division on Earth and Life Studies

National Research Council

NATIONAL ACADEMY PRESS

Washington, D.C. 2001

i

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
NOTICE: The project that is the subject of this report was approved by the Governing Board of the

National Research Council, whose members are drawn from the councils of the National Academy

of Sciences, the National Academy of Engineering, and the Institute of Medicine. The members of

the committee responsible for the report were chosen for their special competences and with regard

for appropriate balance.

Support for this project was provided by the U.S. Environmental Protection Agency under Grant

No. X-826345-01-0.

International Standard Book Number: 0-309-07408-8

Library of Congress Control Number: 2001093985

Classifying Drinking Water Contaminants for Regulatory Consideration is available from the

National Academy Press, 2101 Constitution Avenue, N.W., Washington, D.C. 20418; (800) 624–

6242 or (202) 334–3422 (in the Washington metropolitan area); Internet <http://www.nap.edu>.

Copyright 2001 by the National Academy of Sciences. All rights reserved.

Printed in the United States of America.

ii

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
The National Academy of Sciences is a private, nonprofit, self-perpetuating 

society of distinguished scholars engaged in scientific and engineering 

research, dedicated to the furtherance of science and technology and to their 

use for the general welfare. Upon the authority of the charter granted to it by 

the Congress in 1863, the Academy has a mandate that requires it to advise 

the federal government on scientific and technical matters. Dr. Bruce M. 

Alberts is president of the National Academy of Sciences. 

The National Academy of Engineering was established in 1964, under the 

charter of the National Academy of Sciences, as a parallel organization of 

outstanding engineers. It is autonomous in its administration and in the 

selection of its members, sharing with the National Academy of Sciences the 

responsibility for advising the federal government. The National Academy of 

Engineering also sponsors engineering programs aimed at meeting national 

needs, encourages education and research, and recognizes the superior 

achievements of engineers. Dr. William. A. Wulf is president of the National 

Academy of Engineering. 

The Institute of Medicine was established in 1970 by the National Academy 

of Sciences to secure the services of eminent members of appropriate 

professions in the examination of policy matters pertaining to the health of 

the public. The Institute acts under the responsibility given to the National 

Academy of Sciences by its congressional charter to be an adviser to the 

federal government and, upon its own initiative, to identify issues of medical 

care, research, and education. Dr. Kenneth I. Shine is president of the 

Institute of Medicine. 

The National Research Council was organized by the National Academy of 

Sciences in 1916 to associate the broad community of science and 

technology with the Academy’s purposes of furthering knowledge and 

advising the federal government. Functioning in accordance with general 

policies determined by the Academy, the Council has become the principal 

operating agency of both the National Academy of Sciences and the National 

Academy of Engineering in providing services to the government, the public, 

and the scientific and engineering communities. The Council is administered 

jointly by both Academies and the Institute of Medicine. Dr. Bruce M. 

Alberts and Dr. William. A. Wulf are chairman and vice chairman, 

respectively, of the National Research Council. 

www.national-academies.org

iii

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the originaltypesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained,and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
iv

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
COMMITTEE ON DRINKING WATER CONTAMINANTS

DEBORAH L.SWACKHAMER, Chair, University of Minnesota, Minneapolis

R.RHODES TRUSSELL, Vice Chair, Montgomery Watson, Inc., Pasadena,

California

FRANK J.BOVE, Agency for Toxic Substances and Disease Registry, Atlanta,

Georgia

LAWRENCE J.FISCHER, Michigan State University, East Lansing

WALTER GIGER, Swiss Federal Institute for Environmental Science and

Technology, Zurich

JEFFREY K.GRIFFITHS, Tufts University, Boston, Massachusetts

CHARLES N.HAAS, Drexel University, Philadelphia, Pennsylvania

NANCY K.KIM, Center for Environmental Health, Albany, New York

DAVID M.OZONOFF, Boston University, Massachusetts

REBECCA T.PARKIN, The George Washington University, Washington, D.C.

CATHERINE A.PETERS, Princeton University, New Jersey

JOAN B.ROSE, University of South Florida, St. Petersburg

PHILIP C.SINGER, University of North Carolina, Chapel Hill

PAUL G.TRATNYEK, Oregon Graduate Institute of Science and Technology,

Portland

Staff

MARK C.GIBSON, Study Director

ELLEN A.DE GUZMAN, Senior Project Assistant

v

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
WATER SCIENCE AND TECHNOLOGY BOARD

HENRY J.VAUX, JR., Chair, Division of Agriculture and Natural Resources,

University of California, Oakland

RICHARD G.LUTHY, Vice Chair, Stanford University, California

RICHELLE M.ALLEN-KING, Washington State University, Pullman

GREGORY B.BAECHER, University of Maryland, College Park

JOHN BRISCOE, The World Bank, Washington, D.C.

EFI FOUFOULA-GEORGIOU, University of Minnesota, Minneapolis

STEVEN P.GLOSS, University of Wyoming, Laramie

WILLIAM A.JURY, University of California, Riverside

GARY S.LOGSDON, Black & Veatch, Cincinnati, Ohio

DIANE M.MCKNIGHT, University of Colorado, Boulder

JOHN W.MORRIS, J.W.Morris Ltd., Arlington, Virginia

PHILIP A.PALMER (Retired), E.I.du Pont de Nemours & Co., Wilmington,

Delaware

REBECCA T.PARKIN, George Washington University, Washington, D.C.

RUTHERFORD H.PLATT, University of Massachusetts, Amherst

JOAN B.ROSE, University of South Florida, St. Petersburg

JERALD L.SCHNOOR, University of Iowa, Iowa City

R.RHODES TRUSSELL, Montgomery Watson, Pasadena, California

Staff

STEPHEN D.PARKER, Director

LAURA J.EHLERS, Senior Staff Officer

JEFFREY W.JACOBS, Senior Staff Officer

MARK C.GIBSON, Staff Officer

WILLIAM S.LOGAN, Staff Officer

M.JEANNE AQUILINO, Administrative Associate

PATRICIA A.JONES KERSHAW, Staff Associate

ANITA A.HALL, Administrative Assistant

ELLEN A.DE GUZMAN, Senior Project Assistant

ANIKE L.JOHNSON, Project Assistant

vi

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
BOARD ON ENVIRONMENTAL STUDIES AND

TOXICOLOGY

GORDON ORIANS, Chair, University of Washington, Seattle

JOHN DOULL, Vice Chair, The University of Kansas Medical Center, Kansas

City

DAVID ALLEN, University of Texas, Austin

INGRID C.BURKE, Colorado State University, Fort Collins

THOMAS BURKE, Johns Hopkins University, Baltimore, Maryland

GLEN R.CASS, Georgia Institute of Technology, Atlanta

WILLIAM L.CHAMEIDES, Georgia Institute of Technology, Atlanta

CHRISTOPHER B.FIELD, Carnegie Institution of Washington, Stanford,

California

JOHN GERHART, University of California, Berkeley

J.PAUL GILMAN, Celera Genomics, Rockville, Maryland

DANIEL S.GREENBAUM, Health Effects Institute, Cambridge, Massachusetts

BRUCE D.HAMMOCK, University of California, Davis

ROGENE HENDERSON, Lovelace Respiratory Research Institute,

Albuquerque, New Mexico

CAROL HENRY, American Chemistry Council, Arlington, Virginia

ROBERT HUGGETT, Michigan State University, East Lansing

JAMES FREDERICK KITCHELL, University of Wisconsin, Madison

DANIEL KREWSKI, University of Ottawa, Ontario, Canada

JAMES A.MacMAHON, Utah State University, Logan

CHARLES O’MELIA, Johns Hopkins University, Baltimore, Maryland

WILLEM FREDERIK PASSCHIER, Health Council of the Netherlands, The

Hague

ANN POWERS, Paco University, School of Law, White Plains, New York

KIRK SMITH, University of California, Berkeley

TERRY F.YOSIE, American Chemistry Council, Arlington, Virginia

Staff

JAMES J.REISA, Director

DAVID J.POLICANSKY, Associate Director and Senior Program Director for

Applied Ecology

RAYMOND WASSEL, Senior Program Director for Environmental Sciences

and Engineering

KULBIR BAKSHI, Program Director for Committee on Toxicology

ROBERTA WEDGE, Program Director for Risk Analysis

K.JOHN HOLMES, Senior Staff Officer

vii

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
viii

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Preface

Most people would agree that protecting our drinking water supply by

regulating the maximum allowable content of hazardous contaminants is

desirable. What is of considerable debate is which contaminants should be

regulated, how many should be regulated, and what process will be used to

select them. The U.S. Environmental Protection Agency (EPA) currently has

enforceable National Primary Drinking Water Regulations for more than 80

inorganic and organic chemical, radionuclide, and microbial contaminants and

groups of related contaminants under the Safe Drinking Water Act (SDWA).

The 1996 SDWA Amendments were intended to further this protective

approach by requiring EPA to periodically develop a list of contaminants that

are currently unregulated and may pose a health risk. The agency must then

select from each list—called the Drinking Water Contaminant Candidate List,

or CCL—at least five contaminants for regulatory decisions every five years. In

addition to supporting the mandated development of drinking water regulations,

each CCL is intended to be the source of priority contaminants for the EPA’s

drinking water program as a whole and, to include research, monitoring, and

guidance development. However, the specifics of developing the CCL and the

manner in which the five or more contaminants are ultimately selected for

regulatory decisions are not specified in the legislation.

The EPA requested assistance from the National Research Council (NRC)

in addressing these difficult issues. This project has been conducted in two

phases. The first phase was completed in July 1999 and resulted in two reports.

The first of these, Setting Priorities for Drinking Water Contaminants,

examines past approaches used by federal agencies, state and local

governments, public water utilities, and other organizations for establishing

priorities among drinking water contaminants and other environmental

pollutants. It also recommends a phased decision

PREFACE ix

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
process, associated time line, and related criteria to assist EPA efforts to set

priorities and decide which contaminants already on a CCL should be subjected

to regulation development, increased monitoring, or additional research. The

NRC also convened a workshop on “emerging” drinking water contaminants

and published the proceedings in a second report entitled Identifying Future

Drinking Water Contaminants. Preceding a dozen papers presented by

government, academic, and industry scientists at the workshop is a short

committee report that outlines a conceptual approach to the creation of future

CCLs. In that report, the committee strongly urged EPA to consider the benefits

of a more carefully considered and detailed description of the requirements of a

CCL development process, especially regarding the identification of critical

drinking water contaminants for regulatory activities from among tens of

thousands of potential candidates.

The second phase of the study focused on refining specific methods and

processes to identify and narrow a very broad universe of potential

contaminants into a smaller, more focused list for planning and action by

interested parties. The specific tasks of the second phase are as follows:

1. Identify and evaluate a process to narrow, focus, and prioritize

contaminants from a preliminary list for inclusion on a smaller, more

manageable list of contaminants, including chemical and

microbiological contaminants. The process and methods will include

simple (semi) quantitative tools to cull the broad preliminary list of

contaminants. The tools that are developed to narrow and focus future

drinking water contaminant lists will be tested using validation case

examples of currently regulated contaminants.

2. Explore the feasibility of developing virulence-activity relationships

(VARs, now termed virulence-factor activity relationships or VFARs)

for microbial contaminants. If a scientifically sound basis for developing

VFARs is determined to be feasible, the committee will provide initial

guidance and recommendations for interested parties on the steps

necessary to construct and use VFARs.

3. Time and resources permitting, the committee will provide specific

recommendations of methods for narrowing the broadest universe of

contaminants to a smaller, preliminary contaminant list.

The Committee on Drinking Water Contaminants of the NRC addresses

these three issues in the following report. We have recommended what may be

considered a bold and innovative approach to selecting contaminants for

inclusion on future CCLs. In our second report,

PREFACE x

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
we recommended that the CCL be developed in a two-step process. Initially, the

“universe” of potential drinking water contaminants is identified by considering

many possible categories and sources of contaminants. A preliminary CCL, or

PCCL, is culled by a screening process and expert judgment from this universe.

Then the CCL is selected from the PCCL using a more refined process in

conjunction with expert judgment.

In this report, we provide initial guidance and several recommendations for

how to accomplish the first step in this process. However, the bulk of the

committee’s effort for this final report was focused on providing a detailed

paradigm for selection of the CCL from the PCCL. To this end, we have

recommend that EPA develop and use a set of selected contaminant attributes to

evaluate the likelihood that a contaminant or group of related contaminants

would occur in drinking water at sufficient concentrations or prevalence to pose

a public health risk. To make this determination, we recommend that the agency

use a prototype classification algorithm in conjunction with expert judgment.

Although this approach requires considerable initial investment by EPA, we

feel that it represents a superior approach to relying exclusively on expert

judgment or ranking schemes such as those reviewed in our first report. The

committee has gone so far as to develop a demonstration algorithm to test the

efficacy of this approach, and the results are compelling. Last but not least, the

committee concludes that the construction and eventual use of VFARs within

EPA’s drinking water program is indeed feasible and merits careful

consideration. We also provide some initial guidance and recommendations for

their application herein.

The committee is grateful for the support of this project by Michael

Osinski and his colleagues at EPA’s Office of Ground Water and Drinking

Water. In addition, we would not have been successful in our endeavors without

the contributions of several experts who gave presentations to us during our first

two meetings and aided in focusing our discussions. They include Fred

Hauchman, EPA Office of Research and Development; Robert Clark, EPA

Office of Research and Development; Kenneth Beattie, Oak Ridge National

Laboratory; and Betty Olson, University of California at Irvine.

We have been highly fortunate as a committee to have the significant

contributions and guidance of Mark Gibson, study director of this project and

staff officer in the NRC’s Water Science and Technology Board, and Carol

Maczka, former program director for toxicology and risk assessment of the

NRC’s Board on Environmental Studies and Toxicology.

PREFACE xi

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Ellen de Guzman, senior project assistant in the Water Science and Technology

Board, provided excellent staff support throughout the second phase of this

study. The commitment shown by the NRC staff helped keep the study on time

and make it a success.

This report has been reviewed in draft form by individuals chosen for their

diverse perspectives and technical expertise in accordance with procedures

approved by the NRC’s Report Review Committee. The purpose of this

independent review is to provide candid and critical comments that will assist

the institution in making its published report as sound as possible and to ensure

that the report meets institutional standards for objectivity, evidence, and

responsiveness to the study charge. The review comments and draft manuscript

remain confidential to protect the integrity of the deliberative process. We wish

to thank the following individuals for their review of this report: David

Acheson, Tufts University; Caron Chess, Rutgers University; Gunther Craun,

Gunther F. Craun and Associates; Joseph Delfino, University of Florida; Lynn

Franklin, Pacific Northwest National Laboratory; Erik Olson, National

Resources Defense Council; and, Fred Pontius, Pontius Water Consult-ants, Inc.

Although the reviewers listed above have provided many constructive

comments and suggestions, they were not asked to endorse the conclusions or

recommendations nor did they see the final draft of the report before its release.

The review of this report was overseen by Michael Kavanaugh, Malcolm Pirnie,

Inc., and Frank Stillinger, Bell Laboratories, Lucent Technologies. Appointed

by the National Research Council, they were responsible for making certain that

an independent examination of this report was carried out in accordance with

institutional procedures and that all review comments were carefully

considered. Responsibility for the final content of this report rests entirely with

the authoring committee and the institution.

I would also like to thank three former members of this committee for their

past insights and contributions, many of which carried over into this report:

Branden Johnson, New Jersey Department of Environmental Protection,

Trenton; Michael McGuire, McGuire Environmental Consultants, Inc., Santa

Monica, California; and, Warren Muir, NRC.

Finally, I thank the 13 members of this extraordinary committee. Each one

brought a unique talent and exceptional degree of commitment

PREFACE xii

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
to the tasks at hand. The diversity of perspectives made for enlivened and

enlightening discussion throughout and ultimately led us to the forward-looking

recommendations contained herein. I was honored to be part of it all.

DEBORAH L.SWACKHAMER

Chair, Committee on Drinking Water Contaminants

PREFACE xiii

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
PREFACE xiv

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Contents

Executive Summary 1

1 DRINKING WATER CONTAMINANT CANDIDATE LIST:

PAST, PRESENT, AND FUTURE

20

 Introduction 20

 Development of the 1998 CCL 24

 Implementation Status of the 1998 CCL 29

 Limitations of the First CCL Development Process 35

 Related SDWA Programs 36

 Identifying and Selecting Contaminants for Future CCLs 42

 Perspective of This Report 44

2 SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING

FUTURE CCLs

45

Introduction 45

 Limitations of the First CCL Development Process 45

 Use of Sound Science in Future Regulatory Decisions 49

 Nature of the Task 50

 Risk Perception 53

Protection of Vulnerable Subpopulations 57

Transparency and Public Participation 60

Summary: Conclusions and Recommendations 69

3 THE UNIVERSE OF POTENTIAL CONTAMINANTS TO

THE PRELIMINARY CCL

71

 Introduction 71

 Two-Step Approach 71

 The Universe of Potential Drinking Water Contaminants 73

 Distinguishing the PCCL from the Universe 81

 Guidelines for Developing Screening Criteria 85

 Fate of the PCCL 88

 Summary: Conclusions and Recommendations 89

CONTENTS xv

About this P

DF file: This new digital representation of the original work has been recomposed from X

ML files created from the original paper book, not from the

original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be

retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
4 PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 93

 Introduction 93

Overview of Contaminant Attributes 93

 Lessons Learned in Applying These Criteria 102

 Summary: Conclusions and Recommendations 105

5 PCCL TO CCL: CLASSIFICATION ALGORITHM 107

 Introduction 107

 Overview of Classification Schemes 108

 Illustrative Example of a Prototype Classification Scheme for

CCL Contaminants

115

 Demonstrated Use of the Trained Classifier 136

 Summary: Conclusions and Recommendations 139

6 VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 143

 Introduction 143

 Statement of the Problem 145

 VFAR Analogy to SARs and Quantitative SARs 146

 Framework 147

 Formulating VFARs 148

 Current Level of Genetic Characterization 152

 Virulence and Potency Response—Health Aspects 163

 Persistence Responses 171

 Interpretation of Issues 179

 Feasibility 180

 Conclusions and Recommendations 184

References 186

Acronyms and Abbreviations 208

 Appendixes 

A The European Prioritization Schemes “COMMPS” and

“DYNAMEC,”

211

B Matlab Programs for Contaminant Classification 223

C Biographical Information 234

CONTENTS xvi

About this P

DF file: This new digital representation of the original work has been recomposed from X

ML files created from the original paper book, not from the

original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be

retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Executive Summary

Americans drink millions of gallons of tap water each day, usually with an

unquestioning faith in its safety. Indeed, the provision and management of safe

drinking water throughout the United States have been major triumphs of public

health practice since the turn of the twentieth century. Despite advances in

water treatment, source water protection efforts, and the presence of several

layers of local, state, and federal regulatory protection, many sources of raw and

finished public drinking water in the United States periodically contain

chemical, microbiological, and other types of contaminants at detectable and

sometimes harmful levels. Furthermore, the production and use of new

chemicals that can reach water supplies and the discovery of emerging

microbial pathogens that potentially can resist traditional water treatment

practices and/or grow in distribution systems pose a regulatory dilemma: Where

and how should the U.S. government focus its attention and limited resources to

ensure safe drinking water supplies for the future? The availability of

increasingly powerful analytical methods for the detection and identification of

smaller amounts of chemicals and microorganisms in the environment, many of

them never before detected, complicates these decisions.

To help address these difficult issues, one of the major requirements of the

Safe Drinking Water Act (SDWA) Amendments of 1996 is that the U.S.

Environmental Protection Agency (EPA) publish a list of unregulated chemical

and microbial contaminants and contaminant groups every five years that are

known or anticipated to occur in public water systems and that may pose risks

in drinking water. The first such list, called the Drinking Water Contaminant

Candidate List (CCL), was published in March 1998. The primary function of

the CCL is to provide the basis for deciding whether to regulate at least five

new contaminants from the CCL every five years. However, since additional

research and

EXECUTIVE SUMMARY 1

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
monitoring need to be conducted for most of the contaminants on the 1998

CCL, the list is also used to prioritize these related activities.

This is the third report by the Committee on Drinking Water Contaminants

(jointly overseen by the National Research Council’s [NRC’s] Water Science

and Technology Board and Board on Environmental Studies and Toxicology).

The committee was formed early in 1998 at the request of EPA’s Office of

Ground Water and Drinking Water to provide advice regarding the setting of

priorities among drinking water contaminants in order to identify those

contaminants that pose the greatest threats to public health. The committee is

comprised of 14 volunteer experts in water treatment engineering, toxicology,

public health, epidemiology, water and analytical chemistry, risk assessment,

risk communication, public water system operations, and microbiology.

In its first report, Setting Priorities for Drinking Water Contaminants, the

committee recommended a phased decision-making process, time line, and

related criteria to assist EPA efforts to set priorities and decide which

contaminants already on a CCL should be subjected to regulation development,

increased monitoring, or additional health effects, treatment, and analytical

methods research. That report also includes a review of several past approaches

to setting priorities for drinking water contaminants and other environmental

pollutants. The committee later organized and conducted an NRC workshop on

emerging drinking water contaminants and subsequently published a second

report entitled Identifying Future Drinking Water Contaminants. That report

includes a dozen papers presented at the workshop by government, academic,

and industry scientists on new and emerging microbiological and chemical

drinking water contaminants, associated analytical and water treatment methods

for their detection and removal, and existing and proposed environmental

databases to assist in their proactive identification and potential regulatory

consideration. Notably, the workshop papers are preceded by a short committee

report that provides a conceptual approach to the creation of future CCLs. In

this regard, the committee strongly urged EPA in its second report to consider

the benefits of a more carefully considered and detailed description of the

requirements of a CCL development process.

For this report, EPA asked the committee—which was partially

reconstituted after the second report to include a new chair—to evaluate,

expand, and revise as necessary the conceptual approach to the generation of

future CCLs and any related conclusions and recommendations documented in

the second report. In addition, EPA asked the committee

EXECUTIVE SUMMARY 2

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
to explore the feasibility of developing and using mechanisms for identifying

emerging microbial pathogens (using what the committee now terms virulencefactor activity relationships, or VFARs) for research and regulatory activities—

also as recommended in the second report. The contents, conclusions, and

recommendations in this report are based on a review of relevant technical

literature, information gathered at three committee meetings, and the expertise

of committee members. As in its first two reports, the committee continues to

emphasize the need for expert judgment throughout all CCL-related processes

and for a conservative approach that errs on the side of public health protection.

The committee chose this perspective because public health is the basis for

the SDWA and its amendments. Further, this report takes the position that

scientific disagreements about the public health effects of contaminants and

their relative severity are the norm and do not signal a deviation from sound

science. For example, when data are sparse they may often appear consistent

and coherent, but data gaps usually become evident as a problem is examined

more fully by different methods and from different perspectives. The EPA faces

a challenging task in assessing the available scientific information about

contaminant risks and, based on that assessment, making decisions about which

contaminants should be placed on a CCL for future regulatory and research

consideration. Throughout this process, there is no replacement for policy

judgments by EPA. As in its first report, the committee has purposely declined

to define what constitutes “sufficient” or “adequate” data for making such

decisions because this remains a matter of judgment that will vary with context.

RECOMMENDED APPROACH FOR THE DEVELOPMENT

OF FUTURE CCLS

Because of the time constraints stipulated by the amended SDWA for

publication of the first CCL, EPA was forced to rapidly develop and utilize a

decision-making process for the creation of the 1998 CCL. The committee feels

that the process used to develop the first CCL, although appropriate for the

circumstances at the time, is not suitable as a long-term model. The process

used in the future should be made more defensible and transparent, and its

development should take place with increased opportunities for public input and

comment. Similar comments

EXECUTIVE SUMMARY 3

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
can be made about policy decisions. These limitations are identified and

discussed in Chapter 2 and provide a foundation for much of this report.

In its first report, the committee concluded that a ranking (rule-based)

scheme that attempts to sort a relatively small number of drinking water

contaminants already on a CCL in a specific order for regulation development,

research, or monitoring is not appropriate. However, the committee

subsequently concluded that such ranking schemes may be useful for sorting

larger numbers of potential contaminants to determine which ones should be

included on future CCLs. In its second report, the committee recommended that

EPA develop a two-step process for the creation of future CCLs.

Two-Step Approach

Despite EPA’s constrained resources, the lack of a comprehensive list of

potential drinking water contaminants, and poor or nonexistent data on health

effects, occurrence, and other attributes of the vast majority of potential

contaminants, the committee continues to recommend that EPA develop and

use a two-step process for creating future CCLs as illustrated in 

Figure ES-1. In summary, a broadly defined universe of potential drinking

water contaminants is first identified, assessed, and culled to a preliminary CCL

(PCCL) using simple screening criteria and expert judgment. All PCCL

contaminants are then assessed individually using a “prototype” classification

tool in conjunction with expert judgment to evaluate the likelihood that they

could occur in drinking water at levels and frequencies that pose a public health

risk to create the corresponding CCL. The committee also continues to

recommend that this two-step process be repeated for each CCL

development cycle to account for new data and potential contaminants that

inevitably arise over time. In addition, all contaminants that have not been

regulated or removed from the existing CCL should automatically be

retained on each subsequent CCL.

It is important to note that although the basic concept for the CCL

development approach has not changed, many of the associated guidelines and

recommendations for its design and implementation have necessarily been

revised and expanded in accordance with the most recent committee

deliberations. The committee also notes that the amended SDWA specifically

allows EPA to circumvent the CCL process and issue

EXECUTIVE SUMMARY 4

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE ES-1 Recommended two-step process for developing future CCLs.

interim regulations for any drinking water contaminant that is determined

to pose an “urgent threat” to humans.

Sociopolitical Considerations

The committee recognizes that the development of a PCCL from the

universe of potential drinking water contaminants, as well as the movement

from a PCCL to the corresponding CCL, is a complex task requiring numerous

difficult classification judgments in a context where data are often uncertain or

missing. Due to data gaps and uncertainties, evaluating contaminants using

widely varying data will often entail making assumptions. Because of this

complexity, the committee believes

EXECUTIVE SUMMARY 5

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
that to be scientifically sound as well as publicly acceptable, the process for

developing future CCLs must depart considerably from the process used to

develop the first CCL. The committee recommends that the process for

selecting contaminants for future CCLs be systematic, scientifically sound,

and transparent. The development and implementation of this process

should involve sufficiently broad public participation. Transparency should

be incorporated into the design and development of the classification and

decision-making process for future CCLs in addition to being an integral

component in communicating the details of the process to the public.

Otherwise, the public may perceive the process as subject to manipulation to

achieve or support desired results. Therefore, sufficient information should be

provided so that private citizens can place themselves in a similar position to

decision-makers and arrive at their own reasonable and informed judgments.

This may require making available to the public the software and databases

used in the CCL development process. The central tenet that the public is, in

principle, capable of making wise and prudent decisions should be recognized

by EPA and reflected in the choice of a public participation procedure to help

create future CCLs. A “decide-announce-defend” strategy that involves the

public only after the deliberation process is over is not acceptable. Substantive a

public involvement should occur throughout the design and implementation of

the process. In this regard, EPA should strive to “get the right participation”

(i.e., broad participation that includes the range of interested and affected

parties) as well as to “get the participation right” (e.g., incorporating public

values, viewpoints, and preferences into the process).

The ultimate goal of the contaminant selection process is the protection of

public health by providing safe drinking water to all consumers. To meet this

goal, the selection process must place high priority on the protection of

vulnerable subpopulations as intended by the SDWA Amendments of 1996.

The committee recommends that not only should the definition of

vulnerable subpopulations comply with the amended language of the

SDWA, but it should also be sufficiently broad to protect public health; in

particular, EPA should consider including (in addition to those subgroups

mentioned as examples in the amended SDWA) all women of childbearing age,

fetuses, the immuno-compromised, people with an acquired or inherited genetic

disposition that makes them more vulnerable to drinking water contaminants,

people who are exceptionally sensitive to an array of chemical contaminants,

people with specific medical conditions that make them more susceptible,

EXECUTIVE SUMMARY 6

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
people with poor nutrition, and people experiencing socioeconomic hardships

and racial or ethnic discrimination.

Universe to PCCL

While the contaminants included on the first (1998) CCL certainly merit

regulatory and research consideration, a broader approach to contaminant

selection could potentially identify higher-risk contaminants. Although the

committee was not able to deliberate extensively on the first step of the CCL

development process (going from the universe of potential drinking water

contaminants to a PCCL) due to time constraints, some initial guidance and

several related recommendations can be provided, many of which reiterate and

expand on those made in its second report:

• EPA should begin by considering a broad universe of chemical,

microbial, and other types of potential drinking water contaminants

and contaminant groups (see Table 3–1). The total number of

contaminants in this universe is likely to be on the order of tens of

thousands of substances and microorganisms, given that the Toxic

Substances Control Act inventory of commercial chemicals alone includes

about 72,000 substances. This represents a dramatically larger set of

substances and microorganisms to be considered initially in terms of types

and numbers of contaminants than that used for the creation of the 1998

CCL.

• EPA should rely on databases and lists that are currently available and

under development, along with other readily available information, to

begin identifying the universe of potential contaminants that may be

candidates for inclusion on the PCCL. For example, EPA should

consider using the Endocrine Disruptor Priority-Setting Database (EDPSD)

to help develop future PCCLs (and perhaps CCLs). Although relevant

databases and lists exist for many categories of potential drinking water

contaminants, other categories have no lists or databases (e.g., products of

environmental degradation). Thus, EPA should initiate work on a strategy

for filling the gaps and updating the existing databases and lists of

contaminants for future CCLs. This strategy should be developed with

public, stakeholder, and scientific community input.

EXECUTIVE SUMMARY 7

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
• As an integral part of the development process for future PCCLs and

CCLs, all information used from existing or created databases or lists

should be compiled in a consolidated database to provide a consistent

mechanism for recording and retrieving information on the

contaminants under consideration. Such a database could function as a

“master list” that contains a detailed record of how the universe of potential

contaminants was identified and how a particular PCCL and its

corresponding CCL were subsequently created. It would also serve as a

powerful analytical tool for the development of future PCCLs and CCLs.

As a starting point, the committee recommends that EPA review its

developing EDPSD to determine if it can be expanded and used as this

consolidated database or whether it can serve as a model for subsequent

development of such a database. The (re)design, creation, and

implementation of such a database should be made in open cooperation

with the public, stakeholders, and the scientific community.

• To assist generally in the identification of the universe of potential

contaminants and a PCCL, the committee recommends that EPA

consider substances based on their commercial use, environmental

location, or physical characteristics (see Table 3–5). EPA should be as

inclusive as possible in narrowing down the universe of potential drinking

water contaminants for the PCCL. The committee envisions that a PCCL

would contain on the order of a few thousand individual substances and

groups of related substances, including microorganisms, for evaluation and

prioritization to form a CCL. However, the preparation of a PCCL should

not involve extensive analysis of data, nor should the PCCL itself directly

drive EPA’s research or monitoring activities.

• The committee recommends the use of a Venn diagram approach

(Figure ES-2) to conceptually distinguish a PCCL from the broader

universe of potential drinking water contaminants. Because of the

extremely large size of the universe of potential drinking water

contaminants, well-conceived screening criteria remain to be developed

that can be applied rapidly and routinely by EPA in conjunction with expert

judgment to cull that universe to a much smaller PCCL. Thus, the PCCL

should include those contaminants that are demonstrated to occur or could

potentially occur in drinking water and those that are demonstrated to cause

or could potentially cause adverse health effects.

• Regarding the development of screening criteria for health effects, the

committee recommends that human data and data on

EXECUTIVE SUMMARY 8

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE ES-2 Conceptual approach to identifying contaminants for

inclusion on a PCCL through the intersection of their demonstrated

and potential occurrence in drinking water and their ability to cause

adverse health effects. Note, the sizes of the intersections and rings

are not drawn to scale and do not represent an estimate of the relative

numbers of contaminants in each area.

whole animals be used as indicators of demonstrated health effects and

that other toxicological data and data from experimental models that

predict biological activity be used as indicators of potential health

effects.

• A variety of metrics could be used to develop screening criteria for the

occurrence of contaminants in drinking water. These are identified in a

hierarchical framework in the committee’s first report and include (1)

observations in tap water, (2) observations in distribution systems, (3)

observations in finished water of water treatment plants, (4) observations in

source water, (5) observations in watersheds and aquifers, (6) historical

contaminant release data, and (7) chemical production

EXECUTIVE SUMMARY 9

About this P

DF file: This new digital representation of the original work has been recomposed from X

ML files created from the original paper book, not from the

original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be

retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
data. The committee recommends that the first four of these should be

used as indicators of demonstrated occurrence and information that

comes from items 5 to 7 should be used to determine potential 

occurrence. For commercial chemicals, their potential for occurrence in

drinking water may be estimated using a combination of production

volume information and water solubility (see Figure 3–2). Most likely

occurrence would involve high-production-volume chemicals with high

water solubility.

• Each PCCL should be published and thereby serve as a useful record

of past PCCL and CCL development and as a starting point for the

development of future PCCLs.

• Development of the first PCCL should begin as soon as possible to

support development of the next (2003) CCL; each PCCL should be

available for public and other stakeholder input (especially through

the Internet) and should undergo scientific review.

PCCL to CCL

The intrinsic difficulty of identifying potentially harmful substances or

microorganisms for movement from a PCCL onto a CCL raises the question of

what kind of process or method is best suited to this judgment. As previously

noted, the sorting of perhaps thousands of PCCL contaminants into two discrete

sets—one (the CCL) to probably undergo research or monitoring of some sort

preparatory to an eventual regulatory decision and another much larger set that

will not—is an exercise in classification. The committee considered three broad

types of strategies for accomplishing this task: expert judgment, rule-based

systems, and prototype classifiers (see Chapter 5 for further information).

Based on this review, the committee decided that a prototype classification

approach using neural network or similar methods would seem to be an

innovative and appropriate approach for EPA to consider. This strategy

recognizes that in ordinary practice, one does not usually classify objects on the

basis of a fixed algorithm (such as a rule-based scheme) but instead uses criteria

based on prior classification of examples or prototypes. As such, prototype

classifiers take advantage of the prototyping activity at which humans generally

(and intuitively) excel. In simplest terms, a neural network is a mathematical

representation of the complex network of biological neurons in higher

organisms such as

EXECUTIVE SUMMARY 10

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
humans. Neural networks and similar methods start with prototypes (a “training

set”) that embody the kinds of outcomes one might wish to achieve. In this case,

the training set would consist of chemicals, microorganisms, and other types of

potential drinking water contaminants that clearly belong on the CCL, such as

currently regulated chemicals (if they were not already regulated), and those

that clearly do not, such as some food additives generally recognized as safe by

the U.S. Food and Drug Administration. For each contaminant in the training

set, its “features” or attributes must be characterized. Using the training set, the

neural network constructs the mode of combining and weighting the prototype

attributes that best differentiate between the two categories. Thus, what has

traditionally been accomplished a priori through the use of a ranking scheme

that was most likely designed by experts and required the extensive use of

expert judgment throughout would now be conducted on the basis of data that

differentiate prototype examples. This a posteriori determination of weights on

the basis of features sets prototype classification methods apart from such rulebased methods and expert judgment.

Contaminant Attributes

The committee recommends that EPA develop and use a set of 

attributes to evaluate the likelihood that any particular PCCL contaminant

or group of related contaminants could occur in drinking water at levels

and frequencies that pose a public health risk. These contaminant

attributes should be used in a prototype classification approach, such as

that described in Chapter 5, and in conjunction with expert judgment to

help identify the highest-priority PCCL contaminants for inclusion on a

CCL. A scoring system and related considerations for a total of five health

effect and occurrence attributes are presented in Chapter 4, along with several

“scored” examples of chemicals and microorganisms, to illustrate the utility of

the recommended approach. For health effects, the committee identified

severity and potency as key predictive attributes; prevalence, magnitude, and

persistence-mobility comprise the occurrence attributes.

Although the committee spent a great deal of time deliberating on the

number and type of contaminant attributes that should be used in the

recommended CCL development approach, ultimately it decided that the five

attributes listed above constitute a reasonable starting point for EPA

EXECUTIVE SUMMARY 11

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
consideration. Furthermore, the scoring metrics and related considerations for

each attribute should be viewed in an illustrative manner. Thus, the committee

does not explicitly or implicitly recommend these five (or that there necessarily

should be five) attributes or that the related scoring metrics be directly adopted

for use by EPA. Should EPA choose to adopt a prototype classification

approach to the development of future CCLs, the committee recommends

that options for developing and scoring contaminant attributes should be

made available for public and other stakeholder input and should undergo

scientific review. The committee also makes the following related

recommendations:

• The assessment of severity should be based, when feasible, on plausible

exposures via drinking water. The committee also recommends that EPA

give consideration to different severity metrics such as a ranking through

use of either quality adjusted or disability adjusted life-years lost from

exposure to a contaminant.

• Regarding the assessment of contaminant prevalence, in some cases

(particularly where contaminants have been included on a PCCL on the

basis of potential rather than demonstrated occurrence), insufficient

information will be available to directly assess temporal or spatial

prevalence (or both). Thus, EPA should consider the possibility of

including information on temporal and regional occurrence to help

determine (score PCCL) contaminant prevalence. When prevalence cannot

be assessed, this attribute must then go unscored and the attribute

persistence-mobility used in its stead. The issue of changing (or

incorporating) “thresholds” for contaminant detection, rather than relying

on continually decreasing detection limits, is one that requires explicit

attention and discussion by EPA and stakeholders.

• Because existing and readily available databases may not be sufficient to

rapidly and consistently score health effect and occurrence attributes for

individual PCCL contaminants, all information from existing or created

databases or lists used in the development of a CCL and PCCL should be

compiled in a consolidated database (as previously recommended).

• Contaminant databases used in support of the development of future CCLs

should report summary statistics on all data collected, not only the

quantifiable observations. In this regard, EPA should formalize a process

for reporting means and/or medians from data with large numbers

EXECUTIVE SUMMARY 12

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
of “nondetect” observations. In addition, EPA may want to consider

providing other measures of concentration in water supplies such as the

95th percentile of contaminant concentration.

Developing and Implementing a Prototype Classification Approach

Chapter 5 presents a framework for how existing contaminant data and

past regulatory decisions could be used by EPA to develop a prototype

classification algorithm to determine, in conjunction with expert judgment,

whether or not a particular drinking water contaminant is of regulatory concern.

More specifically, the committee demonstrates how a prototype classification

approach—which must first be “trained” (calibrated) using a training data set

containing both contaminants “presumed worthy of regulatory consideration”

and those that are not—can be used in conjunction with expert judgment to

predict whether a new (PCCL) contaminant should be placed on the CCL or

not. The framework is intended to serve as a model of how EPA might develop

its own prototype classification scheme for the creation of future CCLs. Use of

the majority of currently regulated drinking water contaminants in the training

data set to serve as contaminants presumed to be worthy of regulatory

consideration can be simply described as “making decisions that are consistent

with and build upon what has been done in the past.”

The committee presents two alternative models for use in the scheme—a

linear model and a neural network. Although the neural network performed

better than the linear model (with respect to minimizing the number of

misclassified contaminants), the committee cannot at this time make a firm

recommendation as to which model EPA should use due to uncertainties in the

training data set employed by the committee. Thus, the committee

recommends that EPA explore alternative model formulations and be

cognizant of the dangers of overfitting and loss of generalization. That is,

EPA must be careful to avoid developing undue confidence in the precision of

the training data sets ultimately used and being overzealous in finding an

algorithm that produces no classification error in representing these data. The

committee warns that this will impose “false structure” in the mapping and not

truly capture the functional dependencies. Additionally, the danger of

overfitting is especially present in neural network modeling because of the

tremendous flexibility in the underlying mathematical relationships—resulting

EXECUTIVE SUMMARY 13

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
in a sacrifice of generalization (predictive) ability.

To adopt and implement the recommended approach to the creation of

future CCLs, EPA will have to employ or work with persons knowledgeable of

prototype classification methods and devote appreciable time and resources to

develop and maintain a comprehensive training data set. In this regard, the

committee strongly recommends that EPA greatly increase the size of the

training data set used illustratively in this report to improve predictive

capacity. One way in which EPA can expand the training data set and

classification algorithm is to allow for the expected case of missing data. That

is, purposefully include in the training data set drinking water contaminants for

which the values of some attributes are unknown and develop a scheme that

allows prediction of contaminants for which some attributes are unknown. EPA

will also have to accurately and consistently assign attribute scores for all

contaminants under consideration (i.e., contaminants in the training data set as

well as contaminants to which the prototype classification algorithm will be

applied for a classification determination). To do this, EPA will have to collect

and organize available data and research for each PCCL contaminant and

document the attribute scoring scheme used to help ensure a transparent and

defensible process. As previously recommended, the creation of a consolidated

database that would provide a consistent mechanism for recording and

retrieving information on the contaminants under consideration would be of

benefit. EPA will also have to withhold contaminants from inclusion in the

training data set to serve as validation test cases that can assess the predictive

accuracy of any classification algorithm developed for use in the creation of

future CCLs.

If neural networks are ultimately used by EPA to establish a prototype

classification approach for the creation of future CCLs, the transparency in

understanding which contaminant attributes determine the contaminant category

will be less than that of a linear model or more traditional rule-based scheme.

However, if one acknowledges that the underlying process that maps attributes

into categorical outcomes is very complex, there is little hope that an accurate

rule-based classification scheme can be constructed. The fact that the nonlinear

neural network performed better than the linear classifier is itself a strong

indicator that the underlying mapping process is complex, and it would be a

difficult task for a panel of experts (including this committee) to accurately

specify the rules and conditions of such mapping. Furthermore, the decrease in

transparency from using a neural network is not inherent or arbitrary,

EXECUTIVE SUMMARY 14

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
but rather derives from the difficulty in elucidating the mapping.

The committee notes that the underlying mapping in a neural network

classifier can be examined just as one would conduct experiments to probe a

physical system in a laboratory. Through numerical experimentation, one can

probe a neural network to determine the sensitivity of the output to various

changes in input data. Although a sensitivity analysis was not conducted due to

time constraints, the committee recommends that EPA should use several

training data sets to gauge the sensitivity of the method as part of its

analysis and documentation if a classification approach is ultimately

adopted and used to help create future CCLs.

Finally, the committee emphasizes that it is recommending a prototype

classification approach to be used in conjunction with expert judgment for the

selection of PCCL contaminants for inclusion on future CCLs. Thus,

transparency is less crucial (though no less desired) at this juncture than when

selecting contaminants from the CCL for regulatory activities as discussed in

the committee’s first report.

IDENTIFYING AND ASSESSING EMERGING WATERBORNE

PATHOGENS

As noted in the committee’s first report and discussed in greater detail in

Chapter 6 of this report, the current approach to identifying and controlling

waterborne disease is fundamentally limited in that the identification of

pathogens is traditionally tied to the recognition of an outbreak. The committee

feels strongly that this ongoing practice is not an effective or proactive means

for protecting public health. Furthermore, current regulatory practice requires

that methods to culture organisms of interest be developed before occurrence

data can be gathered. Thus, a microorganism must ordinarily first be identified

as a pathogen, and be capable of in vitro culture, before occurrence data are

collected. This long-standing paradigm makes it very difficult or impossible to

develop a database of potential or emerging pathogens. The committee feels

that this constitutes a severe bottleneck to identifying and addressing potentially

important emerging microbial contaminants in drinking water. Thus, a new

approach to assessing pathogens could help overcome this serious and ongoing

problem.

EXECUTIVE SUMMARY 15

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Virulence-Factor Activity Relationships

A virulence-factor activity relationship is the known or presumed linkage

between the biological characteristics of a microorganism and its real or

potential ability to cause harm (pathogenicity). The term is rooted in a

recognition of the utility of using (quantitative) structure-activity relationships

(QSARs or SARs) to compare the structure of new chemicals to known

chemicals to enable prediction of their toxicity. Chapter 6 of this report

responds to EPA’s request that the committee explore the feasibility of

developing VFARs for their construction and use in EPA’s drinking water

program. Furthermore, the committee provides a framework, initial guidance,

and recommendations on the necessary steps for their construction and use to

help identify emerging waterborne pathogens and predict their ability to cause

disease in exposed humans.

For pathogenic microorganisms, besides the cell or organism itself, there

are many levels of morphological components that can sometimes be used to

identify pathogens. In addition to these large structures, there are smaller

biochemical components including proteins, carbohydrates, and lipids that are

related directly to the virulence of a particular microorganism. (In this report,

virulence is defined broadly as the quality of being poisonous or injurious to life

[i.e., virulent].) Some examples of these biochemical components include the

outer coat of some bacteria (the lipid polysaccharide coat), attachment and

invasion factors, and bacterial toxins. Together, these structures and compounds

can generally be termed “virulence factors” and the blueprints for them are the

genetic code of an organism. For this reason, a principal topic of Chapter 6 is

the genetic structure of various microorganisms because of its direct

relationship to virulence factors.

Owing to recent advances in molecular biology, the genetic structures of

many thousands of microorganisms (especially bacteria and viruses) have been

identified, reported, and stored in what are commonly called gene banks.

Sophisticated computer programs allow for the sorting and matching of genetic

structures and specific genes. The discipline that organizes and studies these

genes is known as bioinformatics. Two other growing areas of related interest

are functional genomics (i.e., understanding the specific role of genes in terms

of the function of the organism) and proteomics (i.e., the science related to the

study of the proteins made when the genomic blueprint is actually translated

into functional molecules). The need and the ability to use these tools to address

EXECUTIVE SUMMARY 16

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
microbial contamination of drinking water are also reviewed in Chapter 6.

Framework

The central concept is to use microbial characteristics to predict virulence

through VFARs. Microbial VFARs would function in much the same way as

QSARs do—that is, to assist in the early identification of at least several

potential elements of virulence. Research has increasingly shown certain

common characteristics of virulent pathogens such as the production of specific

toxins, specific surface proteins, and specific repair mechanisms that enhance

their ability to infect and inflict damage in a host. Recently some of these

“descriptors” (terminology often used in QSARs) have been tied to specific

genes, and it has become evident that the same can be done for other descriptors

as well. Identification of these descriptors, either directly or through analysis of

genetic databases, could become a powerful tool for estimating the potential

virulence of a microorganism. This is particularly true for two important aspects

of virulence: potency and persistence in the environment. The committee

conceives of VFAR as being the relationship that ties specific descriptors to

outcomes of concern (see Figure ES-3).

FIGURE ES-3 Schematic drawing of VFAR predicting outcomes of concern

(virulence, potency, persistence) using the presence or quality of descriptor

variables.

EXECUTIVE SUMMARY 17

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Feasibility

For the VFAR concept to be ultimately adopted and used by EPA in the

agency’s drinking water program, it must be feasible. This committee strongly

believes this to be the case. Chapter 6 includes a review of several aspects

concerning feasibility, including scientific validity and applicability; actual

technological feasibility; the application of these technologies to studying

disease in humans (validation); the degree to which these methodologies are

being universally adopted within the scientific community; and the need for

their development and use to adhere to the principles of transparency, public

participation, and other sociopolitical considerations reviewed in Chapter 2. To

one extent or another, each of these elements affects the ability of the VFAR

concept to be developed, used, or validated. These elements either are present

or can reasonably be expected to be available in the near future, so the

committee concludes that the use of VFARs is indeed feasible.

While the technology, methodology, and even the genetic databanks exist,

the application of a VFAR approach to assess waterborne pathogens would

require considerable effort and expenditure of resources by EPA in conjunction

with the Centers for Disease Control and Prevention, National Institutes of

Health, and other federal and state health organizations. Such a “Waterborne

Microbial Genomics” project would also require extensive expertise in

bioinformatics, molecular microbiology, environmental microbiology, and

infectious disease.

The committee fully recognizes that even the initial development of such a

program (excluding its maintenance and expansion) is likely to require at least a

five-year commitment and significant cooperation and expenditure of resources

by EPA and other participating organizations. However, the opportunities for

rapid identification of microbial hazards in water afforded by such a program

would greatly improve the ability of EPA to quickly and successfully protect

public health and improve water quality.

VFAR Conclusions and Recommendations

Despite the identification and discussion of some necessary caveats and

limitations, the committee concludes that the construction and eventual use of

VFARs in EPA’s drinking water program is feasible and merits careful

consideration. More specifically, the committee makes the following

recommendations:

EXECUTIVE SUMMARY 18

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
• Establish a scientific VFAR Working Group on bioinformatics,

genomics, and proteomics, with a charge to study these disciplines on

an ongoing basis and periodically inform the agency as to how these

disciplines can affect the identification and selection of drinking water

contaminants for future regulatory, monitoring, and research

activities. The committee acknowledges the importance of several practical

considerations related to the formation of such a working group within

EPA, including how it should be administered and supported (e.g.,

logistically and financially) or where it could be located. However, the

committee did not have sufficient time in its meetings to address these

issues or make any related recommendations.

• The findings of this report and those of the Biotechnology Research

Working Group (BRWG, 2000: Interagency Report on the Federal

Investment in Microbial Genomics) should be made available to such a

working group at its inception. The committee views the activities of a

VFAR Working Group as a continuing process in which developments in

the fields of bioinformatics, genomics, and proteomics can rapidly be

assessed and adopted for use in EPA’s drinking water program.

• The working group should be charged with the task of delineating

specific steps and related issues and time lines needed to take VFARs

beyond the conceptual framework of this report to actual development

and implementation by EPA. All such efforts should be made in open

cooperation with the public, stakeholders, and the scientific community.

• With the assistance of the VFAR Working Group, EPA should identify

and fund pilot bioinformatic projects that use genomics and

proteomics to gain practical experience that can be applied to the

development of VFARs while it simultaneously dispatches the charges

outlined in the two previous recommendations.

• EPA should employ and work with scientific personnel trained in the

fields of bioinformatics, genomics, and proteomics to assist the agency

in focusing efforts on identifying and addressing emerging waterborne

microorganisms.

• EPA should participate fully in all ongoing and planned U.S. 

government efforts in bioinformatics, genomics, and proteomics as 

potentially related to the identification and selection of waterborne 

pathogens for regulatory consideration.

EXECUTIVE SUMMARY 19

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
1

Drinking Water Contaminant Candidate

List: Past, Present, and Future

INTRODUCTION

The provision of safe drinking water throughout the United States has been

a major triumph in U.S. public health practice since the turn of the twentieth

century. The quality of this essential service is critical to community health

because not only does it provide a life-giving substance to our communities, but

it also has the potential to deliver harmful substances and microorganisms if not

properly maintained. Maintenance of drinking water quality has been

accomplished through several layers of federal, state, and local government

laws, advisories, and regulations designed to protect public water supplies.

Despite multiple levels of regulatory protection, however, many sources of raw

and treated public drinking water in the United States contain chemical,

microbiological, and even radiological contaminants at detectable and

occasionally harmful levels (EPA, 1999a; Neal, 1985). The continuing presence

of contaminants in water supplies, as well as documented outbreaks of

waterborne disease and the many other outbreaks thought to go undetected,

serve as a clear reminder that unprotected and contaminated drinking water can

still pose health risks to the population (NRC, 1999a). Continuing public health

vigilance is necessary to ensure that drinking water contaminants, especially

newly identified ones, are appropriately addressed.

Perhaps the most important, comprehensive, and widely enforced law

designed to protect the public from hazardous substances in drinking water is

the Safe Drinking Water Act (SDWA). Enacted in 1974, it was significantly

amended in 1986 and again most recently in 1996 and is administered by the

U.S. Environmental Protection Agency (EPA). Prior to the passage of the

original SDWA, the only enforceable federal drinking water standards were

directed at waterborne pathogens in water supplies

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

20

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
utilized by interstate carriers such as buses, trains, airplanes, and ships (NRC,

1997). The reader should refer to the 1997 National Research Council (NRC)

report Safe Water from Every Tap: Improving Water Service to Small

Communities (NRC, 1997) for an abbreviated review of the historical

development of drinking water supply regulations in the United States.

Alternatively, Pontius and Clark (1999) provide an extensive overview and

discussion of this topic, especially as related to the SDWA and its subsequent

amendments.

The purpose of the original SDWA was to ensure that public water systems

(PWSs)1 meet national primary drinking water regulations2 for contaminants to

protect public health. The SDWA also established a joint federal-state system to

help administer the nationwide program and ensure compliance with federal

standards. It is important to note that the SDWA does not regulate bottled

water. Rather, bottled water is regulated at the federal level by the U.S. Food

and Drug Administration under authority of the Federal Food, Drug, and

Cosmetic Act. This report is concerned principally with requirements newly

established in the SDWA Amendments of 1996.

Among other changes, the amended SDWA requires EPA to publish a list

of unregulated contaminants and contaminant groups every five years that are

known or anticipated to occur in public water systems and which may require

regulation. This list, the Drinking Water Contaminant Candidate List—

commonly referred to as the CCL—will provide the

1 Public water systems subject to regulation under the amended SDWA are defined as

distribution systems that provide water for human consumption through “constructed

conveyances” (e.g., pipe networks, irrigation ditches) to at least 15 service connections

or an average of 25 individuals daily for at least 60 days per year (EPA, 1998b). 2 For chemical contaminants, a national primary drinking water regulation includes a

nonenforceable criterion called the maximum contaminant level goal (MCLG) that is

used to help set an enforceable standard called the maximum contaminant level (MCL)

or treatment techniques (if contaminant monitoring is deemed not feasible). In general,

MCLs are set as close to the MCLG as feasible, depending on risk management

considerations such as an EPA determination that the cost of a setting an MCL at the

MCLG is not justified by the benefits (EPA, 1996a). For microbiological contaminants,

the original SDWA philosophically established a zero tolerance for disease-causing

organisms as the health goal (i.e., the MCLG is set at zero). In practice, however,

treatment performance techniques, rather than specific allowable concentrations of

pathogens (such as MCLs), historically have served as the basis for regulating microbial

contaminants in drinking water (NRC, 1999a).

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

21

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
basis for a mandated EPA decision to regulate (or not) at least five new

contaminants every five years.3 EPA published the first draft CCL on October

6, 1997 (EPA, 1997a), and the first final CCL on March 2, 1998, hereafter

referred to as the 1998 CCL (EPA, 1998a). The 1998 CCL is comprised entirely

of chemical and microbial contaminants and contaminant groups. In addition to

supporting the mandated development of drinking water regulations, each CCL

is intended to be the source of priority contaminants for the agency’s drinking

water program as a whole, including research, monitoring, and guidance

development.

The Committee on Drinking Water Contaminants (jointly overseen by the

NRC’s Water Science and Technology Board and Board on Environmental

Studies and Toxicology) was formed in 1998 at the request of EPA’s Office of

Ground Water and Drinking Water to provide advice regarding the setting of

priorities among drinking water contaminants in order to identify those

contaminants that pose the greatest threats to public health. The original

committee consisted of 14 volunteer experts in water treatment engineering,

toxicology, public health, epidemiology, water and analytical chemistry, risk

assessment, risk communication, public water system operations, and

microbiology.

The committee’s activities have been conducted in two discrete phases

over a three-year period. During the first phase of the study (February 1998

through July 1999), the committee convened twice, leading to the development

of its first report, Setting Priorities for Drinking Water Contaminants (NRC,

1999a). That report recommended a phased decision process, time line, and

related criteria to assist EPA efforts to set priorities and decide which CCL

contaminants should be subjected to regulation development, increased

monitoring, or additional health effects, treatment, and analytical methods

research. It also includes a review of several past approaches to setting priorities

for drinking water contaminants and other environmental pollutants. First and

foremost, the report emphasizes the need for expert judgment throughout this

process

3 It is important to note that Section 1412(b)(1)(d) of the amended SDWA allows EPA

(after consultation with the U.S. Department of Health and Human Services) to issue

interim regulations for any drinking water contaminant that is determined to pose an

“urgent threat” to human health without adhering to the newly revised process for

making regulatory decisions (i.e., the CCL process) or completing a cost-benefit

analysis. However, a cost-benefit analysis and the required determination to regulate or

not must be completed within three years after the interim rule, and the rule must be

repromulgated or revised if necessary.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

22

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
and for a conservative approach that errs on the side of public health protection.

During the first phase of study, the committee also organized and

conducted an NRC workshop on emerging drinking water contaminants and

subsequently published a second report entitled Identifying Future Drinking

Water Contaminants (NRC, 1999b). That report includes a dozen papers

presented at the workshop by government, academic, and industry scientists on

new and emerging microbiological and chemical drinking water contaminants,

associated analytical and water treatment methods for their detection and

removal, and existing and proposed environmental databases to assist in their

proactive identification and potential regulation. The workshop papers are

preceded by a short committee report that provides a conceptual approach to the

creation of future CCLs. The committee strongly urged EPA to consider the

benefits of a more careful, detailed assessment of the CCL development

process, especially regarding the identification of critical drinking water

contaminants for regulatory activities from among tens of thousands of potential

candidates.

For the second phase of the study (August 1999 through February 2001),

EPA asked the committee—which was partially reconstituted, including a new

chair—to evaluate, expand, and revise as necessary the conceptual approach for

the generation of future CCLs and related conclusions and recommendations

documented in the committee’s second report. EPA also asked the committee to

explore the feasibility of developing mechanisms for identifying emerging

microbial pathogens (using what the committee now terms virulence-factor

activity relationships, or VFARs; see Chapter 6 for further information) for

research and regulatory activities as recommended in the committee’s second

report.

This chapter provides a brief summary of the purpose, development, and

implementation status of the 1998 CCL, an overview of the recommended twostep process for the generation of future CCLs, and an overview of two closely

related programs required by the amended SDWA. Chapter 2 describes

important sociopolitical issues that EPA should consider when prioritizing

contaminants for inclusion on future CCLs. Chapter 3 provides some initial

recommendations for conducting the first step of the CCL development process.

Chapter 4 describes in detail the second step of the recommended CCL

development process and includes a general discussion of how EPA can

implement the recommended approach. Chapter 5 builds on Chapter 4 and

provides an overview and pros and cons of several classification approaches

that EPA could use to help develop future CCLs. It also provides and discusses

the results of

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

23

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
the committee’s attempt to demonstrate and validate the utility of the

recommended approach. Lastly, Chapter 6 responds to EPA’s request that the

committee explore the feasibility of developing VFARs as a tool to help

identify emerging waterborne pathogens and provides some initial guidance and

recommendations on the necessary steps for their construction and use.

DEVELOPMENT OF THE 1998 CCL

Shortly after passage of the 1996 SDWA Amendments and prior to the

development of the first CCL, EPA began work on a conceptual, risk-based

approach to identifying and selecting unregulated chemical and microbiological

drinking water contaminants as priorities for its drinking water program. This

conceptual approach, called the contaminant identification method (CIM), was

intended to identify and classify potential contaminants into several possible

regulatory and nonregulatory categories for future activities (EPA, 1996b).

These categories included contaminants to be placed on the CCL (for future

regulatory determinations), those requiring further toxicological research, those

recommended for monitoring, those needing health advisory development or

other guidance, and those for which no action was required. The CIM was also

intended to be used to reevaluate currently regulated drinking water

contaminants as periodically required under the amended SDWA. However,

extensive work on the CIM was suspended shortly after its inception due to the

time constraints stipulated by the SDWA Amendments for publication of the

first CCL. Instead, EPA relied primarily on the advice of the National Drinking

Water Advisory Council4 (NDWAC) Working Group on Occurrence and

Contaminant Selection5 for developing the

4 NDWAC was chartered under the original SDWA and established in 1975 under the

authority of the Federal Advisory Committee Act to provide independent advice and

recommendations to EPA on drinking water issues and SDWA policies (EPA, 1999e). Its

15 members are appointed by the Office of Ground Water and Drinking Water, represent

a broad base of interests and expertise, and serve staggered three-year terms. Several

working groups were formed within NDWAC after the SDWA Amendments of 1996 to

assist EPA in the implementation of many of its new and revised statutory requirements.

Further information about NDWAC can be obtained at http://www.epa.gov/safewater/

ndwac/council.html.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

24

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
draft 1998 CCL.

At the first meeting of the working group, EPA proposed a total of 391

contaminants (including 25 microorganisms) taken from 10 lists of potential

drinking water contaminants as a reasonable starting point for developing the

draft CCL (EPA, 1997a). A total of eight lists—most originating from a variety

of EPA programs (see Table 1–1) —and 262 chemicals and chemical groups

were ultimately retained and evaluated by EPA. EPA also specifically deferred

consideration of 21 contaminants for the draft CCL based solely on the

possibility of their being endocrine disruptors and of 35 pesticides pending

further evaluation of their potential to occur at levels of health concern (see

Tables 4–2 and 4–3 of NRC, 1999a). At the recommendation of the working

group, EPA first evaluated each chemical according to whether it had

demonstrated or potential occurrence in drinking water (EPA, 1997a). Only

those contaminants that met either of two criteria for occurrence were

subsequently and similarly evaluated for evidence or suspicion that they cause

adverse health effects via drinking water or other exposure routes.

Also at the recommendation of the working group, EPA sought external

expertise in identifying and selecting potential waterborne pathogens for

inclusion on the draft 1998 CCL (EPA, 1997a). For this purpose, EPA

convened a workshop of microbiologists and public health specialists and

provided an initial list of microorganisms for their immediate consideration

(EPA, 1997b). The list included bacteria, viruses, protozoa, and algal toxins

selected on the basis of disease outbreak data, published literature documenting

the occurrence of known or suspected waterborne pathogens, and other related

information. All of the microorganisms included on the initial list, as well as

other potential microbiological contaminants that arose during deliberations,

were evaluated individually against a set of baseline criteria related to an

organism’s (1) public health significance, (2) known waterborne transmission,

(3) occurrence in source water, (4) effectiveness of current water treatment, and

(5) adequacy of analytical methods as developed by the workshop participants.

The evaluation also assessed the basic research and data needs for each

microorganism. When published, the draft CCL included every microbiological

contaminant recommended by the workshop participants and subsequently

adopted by the full NDWAC.

5 The NDWAC Working Group on Occurrence and Contaminant Selection consisted

of engineers, microbiologists, toxicologists, and public health scientists from government

agencies, water utilities, and other stakeholder groups.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

25

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 1–1 Chemical Lists Considered for Development of Draft 1998 CCL

List Summary and Notes

1991 Drinking Water Priority List

(DWPL)

Excluding disinfection by-products for

which regulations were being

developed under the Disinfectants and

Disinfection Byproducts Rule

Health advisories (HAs) All contaminants with HAs or HAs

under development by EPA

Integrated Risk Information System

(IRIS)

Contaminants adopted from IRIS based

on a risk-based screen developed by

EPA in anticipation of the 1994 DWPL

Contaminants identified by-public water

systems

List of non-target contaminants

identified in public water systems in

anticipation of the 1994 DWPL

List of contaminants found at

Comprehensive Environmental

Response, Compensation, and Liability

Act (CERCLA) sites

Top contaminants from a 1995

CERCLA list of prioritized hazardous

substances

Stakeholder summary list Contaminants proposed by participants

in a December 2–3, 1997, stakeholder

meeting on EPA’s CIM

Toxic Release Inventory (TRI) list Chemicals that met criteria for

assessing the potential of a

contaminant to occur in public water;

derived from a 1994 TRI list of 343

chemicals

Office of Pesticide Programs (OPP)

ranking

Pesticides and degradates taken from

OPP ranking of pesticides from highest

to lowest potential to reach groundwater

SOURCE: Adapted from EPA, 1997a; NRC, 1999a.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

26

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
The draft 1998 CCL included 58 unregulated6 chemical and 13

microbiological contaminants and contaminant groups (chemical contaminants

were further divided into preliminary data need categories such as those

requiring additional health effects data but not occurrence data) and was made

publicly available for comments in the Federal Register (EPA, 1997a). EPA

considered all comments, data, and other information provided by the public

and several stakeholder groups in preparing the final CCL.

The 1998 final CCL (EPA, 1998a) comprises 60 contaminants and

contaminant classes, including 10 microbial contaminants and groups of related

microorganisms and 50 chemicals and chemical groups, as alphabetically listed

in Table 1–2. A total of four microorganisms and eight chemicals and chemical

groups were removed from the draft CCL. However, one chemical (perchlorate)

and one broad group of microorganisms (cyanobacteria, other freshwater algae,

and their toxins) were added based on public comments and the continued input

of the working group. Modifications to the draft CCL were also reviewed and

formally approved by the full NDWAC prior to publication of the final 1998

CCL.

With the exception of sulfate (see footnote 6), the CCL includes

contaminants that are not currently subject to any proposed or promulgated

primary drinking water regulation, but are known or anticipated to occur in

public water systems and may require regulation under the SDWA (EPA,

1998a). Thus, the 1998 CCL is intended to be the primary source of priority

contaminants for future regulatory actions by EPA’s drinking water program

until the next CCL is published in 2003. Figure 1–1 summarizes the current

time line for the development, promulgation, and implementation of the 1998

CCL and future CCLs and two other related programs required under the

amended SDWA that are described later in this chapter. For further information

on the development of the 1998 CCL, please refer to Chapter 4 of the

committee’s first report, Setting Priorities for Drinking Water Contaminants

(NRC, 1999a).

6 In accordance with the SDWA Amendments of 1996, all contaminants on the 1998

CCL were not subject to any proposed or promulgated national primary drinking water

regulation, with the exception of nickel, aldicarb and its degradates, and sulfate, which

were included because of prior obligations to complete regulatory action for them (EPA,

1997a).

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

27

About this P

DF file: This new digital representation of the original work has been recomposed from X

ML files created from the original paper book, not from the

original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be

retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 1–2 1998 Drinking Water Contaminant Candidate List (CCL)

Microbiological Contaminants

Acanthamoeba (guidance)

Adenoviruses

Aeromonas hydrophila

Caliciviruses

Coxsackieviruses

Cyanobacteria (blue-green algae), other freshwater algae, and their

toxins

Echoviruses

Helicobacter pylori

Microsporidia (Enterocytozoon and Septata)

Mycobacterium avium intracellulare

Chemical Contaminants CASRNa

1,1,2,2-Tetrachloroethane 79–34–5

1,2,4-Trimethylbenzene 95–63–6

1,1-Dichloroethane 75–34–3

1,1-Dichloropropene 563–58–6

1,2-Diphenylhydrazine 122–66–7

1,3-Dichloropropane 142–28–9

1,3-Dichloropropene 542–75–6

2,4,6-Trichlorophenol 88–06–2

2,2-Dichloropropane 594–20–7

2,4-Dichlorophenol 120–83–2

2,4-Dinitrophenol 51–28–5

2,4-Dinitrotoluene 121–14–2

2,6-Dinitrotoluene 606–20–2

2-Methyl-phenol (o-cresol) 95–48–7

Acetochlor 34256–82–1

Alachlor ESA and other acetanilide pesticide degradation products N/A

Aldrin 309–00–2

Aluminum 7429–90–5

Boron 7440–42–8

Bromobenzene 108–86–1

DCPA mono-acid degradate 887–54–7

DCPA di-acid degradate 2136–79–0

DDE 72–55–9

Diazinon 333–41–5

Dieldrin 60–57–1

Disulfoton 298–04–4

Diuron 330–54–1

EPTC 759–94–4

Fonofos 944–22–9

Hexachlorobutadiene 87–68–3

p-lsopropyltoluene (p-cymene) 99–87–6

Linuron 330–55–2

Manganese 7439–96–5

Methyl bromide 74–83–9

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

28

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Chemical Contaminants CASRNa

Metolachlor 51218–45–2

Metribuzin 21087–64–9

Molinate 2212–67–1

MTBE 1634–04–4

Naphthalene 91–20–3

Nitrobenzene 98–95–3

Organotins N/A

Perchlorate N/A

Prometon 1610–18–0

RDX (1,3,5-trinitrohexahydro-s-triazine) 121–82–4

Sodium 7440–23–5

Sulfate 14808–79–8

Terbacil 5902–51–2

Terbufos 13071–79–9

Triazines and degradation product of triazines (including, but not

limited to Cyanizine [21725–46–2], and atrazine-desethyl [6190–65–

4]

N/A

Vanadium 7440–62–2

NOTE: DCPA=(Dacthal) dimethyl-2,3,5,6-tetrachlorobenzene-1,4-dicarboxylate; DDE=1,1-

dichloro-2,2,-bis(p-diclorodiphenyl) ethylene; EPTC=S-ethyl dipropylthiocarbamate; MTBE=methylt-butyl ether; RDX=royal Dutch explosive.

a

Chemical Abstracts Service Registry Number.

SOURCE: EPA, 1998a.

IMPLEMENTATION STATUS OF THE 1998 CCL

As noted by EPA, sufficient data are necessary to analyze the extent of

exposure and risk to populations (particularly for vulnerable subpopulations

such as infants and immuno-compromised persons as mandated by the amended

SDWA) via drinking water in order to determine appropriate regulatory action

(EPA, 1998a, 2000b). If sufficient data are not available, additional data must

be obtained before any meaningful assessment can be made for a specific

contaminant. In this regard, a table listing several categories of preliminary data

needs for all chemicals on the draft 1998 CCL (EPA, 1997a) was expanded to

include the microorganisms on the final CCL. These preliminary data need

categories are now collectively called future action (“next-step”) categories

(EPA, 1998a). All 1998 CCL contaminants are currently divided into one or

more of these categories (see Figure 1–2 and Table 1–3), which are used to help

set priorities for EPA’s drinking water program. It is important to note that there

has been periodic reassignment of contaminants into and

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

29

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE 1–1 Current time line and interaction of selected major regulatory

requirements of the SDWA Amendments of 1996. SOURCE: Adapted from

EPA, 1997c; NRC, 1999a.

FIGURE 1–2 The 1998 CCL and next step categories as of June 2000.

SOURCE: Adapted from EPA, 1999d, 2000b.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

30

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
out of these categories since publication of the draft 1998 CCL as

additional data have been obtained and evaluated. The current “regulatory

determination priorities” category shown in Table 1–3 includes those

contaminants considered to have sufficient data to evaluate both exposure and

risk to public health and to support a regulatory decision (EPA, 2000b). Thus,

the contaminants in this next-step category will be used to select five or more

contaminants for which EPA will make a determination to regulate or not by

August 2001. This category also includes those contaminants (sodium and

Acanthamoeba) for which EPA intends to develop nonenforceable guidance

rather than drinking water regulations. At present, only those contaminants in

the regulation determination priorities category that ultimately receive a

decision to be regulated, not regulated, or issued a health advisory will be

removed entirely from the CCL process (i.e., not be retained on the 2003 CCL).

As noted previously by the committee (NRC, 1999a), the first CCL began

as an essentially unranked list of research needs for drinking water

contaminants. Additional research and monitoring must be conducted for many,

if not most, of the contaminants on the current CCL as indicated in Table 1–3

(EPA, 2000b). Thus, EPA faces a daunting task in assessing the available

scientific information about CCL contaminant risks and, based on that

assessment, making a risk management decision about which contaminants

should be moved off one of the research lists and into regulatory action.

Research Plan for 1998 CCL

Since its publication in March 1998, however, EPA has made progress in

setting an overall CCL research strategy and schedule through its Office of

Research and Development (ORD) which has the overall responsibility for

shaping and guiding the agency’s research agenda. The overall goal of ORD’s

CCL research process is to provide appropriate information for the Office of

Water’s (OW’s) regulatory determinations concerning CCL contaminants. More

specifically, this research is intended to identify the scientific and engineering

data needed and to characterize the risks posed by 1998 CCL contaminants. It is

beyond the scope of this report to describe the CCL research strategy in great

detail. However, the committee notes that several elements from its first report

(NRC, 1999a) for setting regulatory and research priorities for contaminants

already on a CCL have been incorporated by EPA and an overview

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

31

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

32

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

33

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
of the development and current status of EPA’s CCL Research Plan is

provided below.

A draft version of the research plan (EPA, 2000b) was currently under

review by EPA’s Science Advisory Board (SAB) and available at the time this

report was written. Notably, the CCL Research Plan (report) is expected to be

revised significantly in response to the SAB review (Fred Hauchman, EPA,

personal communication, 2000). The CCL Research Plan report addresses five

major questions:

1. What is EPA’s plan for identifying and ranking research needs for the

1998 CCL contaminants?

2. What analytical methods are needed to adequately address occurrence,

exposure, health effects, and treatability issues?

3. What are the occurrence and exposure issues associated with CCL

contaminants in source water, finished water, and drinking water

distribution systems?

4. Are there significant health risks associated with exposure to CCL

contaminants?

5. How effective are candidate treatment technologies for controlling CCL

contaminants?

EPA decided on a two-phased approach to form the basis for the 1998

CCL Research Plan (EPA, 2000b). Phase I is a screening level effort in which

the CCL contaminants are evaluated with regard to available methods, health

risk, and treatment information. This screening process involves the

examination of minimum data sets that can be used to determine if a

contaminant should be moved into the regulatory determination priorities

category of the CCL or moved into Phase II. In Phase II, a more in-depth

examination is conducted to determine whether the contaminant should be

recommended for regulation, guidance should be developed, or a

recommendation not to regulate should be made. In general, Phase II research

involves the generation of a comprehensive database for each CCL contaminant

on its health effects, analytical methods, occurrence, exposure, and treatment

options. Because of the complexity of the CCL research process and the need to

integrate ORD’s efforts, an Implementation Team comprised of researchers and

managers from ORD’s laboratories and centers as well as representatives from

OW will be established. The team will be responsible for providing oversight

and coordinating the CCL research process while balancing the agency’s

resource commitments against the requirements of the CCL process.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

34

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
The CCL Research Plan was developed by EPA in close consultation with

outside stakeholders, including the American Water Works Association

(AWWA), the AWWA Research Foundation (AWWARF), other government

agencies,7 universities, and other public and private sector groups (EPA,

2000b). In addition, several expert workshops that helped in developing the

1998 CCL itself were used to help identify research needs for specific

contaminants. For example, EPA and AWWARF jointly sponsored a three-day

conference in September 1999 that was intended to review all aspects of the

proposed CCL Research Plan and make suggestions for future research

activities (AWWARF, 2000). Representatives attended the meeting from the

water utility industry, state and federal health and regulatory agencies,

professional associations, academia, and public interest groups, and

recommendations and results from the meeting were incorporated into the CCL

Research Plan.

After the AWWARF-EPA workshop in September 1999, a special panel

was convened by EPA to examine the risk assessment and risk characterization

issues associated with the CCL Research Plan. Recommendations from that

panel were utilized in developing the two-phased research approach outlined in

the CCL Research Plan report. Implementation of the CCL Research Plan will

require the coordinated efforts of both government and nongovernment entities,

as did its creation (EPA, 2000b). In this regard, EPA intends to make all aspects

of 1998 CCL research planning, implementation, and communication a

collaborative process through a series of public workshops and stakeholder

meetings held periodically over the next few years.

LIMITATIONS OF THE FIRST CCL DEVELOPMENT

PROCESS

EPA stated in the draft 1998 CCL that the “first CCL is largely based on

knowledge acquired over the last few years and other readily available

information, but an enhanced, more robust approach to data collection and

evaluation will be developed for future CCLs” (EPA, 1997a). Several public

commenters on the draft CCL also noted the need for a more systematic and

scientifically defensible approach to selecting contaminants for future CCLs

(EPA, 1998c). Chapter 4 of the committee’s

7Centers for Disease Control and Prevention, National Institute of Environmental

Health Sciences, and National Institute for Occupational Safety and Health.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

35

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
first report (NRC, 1999a) summarizes the development of the first CCL in

detail. The committee’s second report (NRC, 1999b) briefly described some

limitations of the first CCL development process before presenting its

recommended conceptual approach to the development of future CCLs.

Chapter 2 of this report more fully describes these and other limitations,

especially as related to various sociopolitical issues surrounding the

development of future CCLs.

Partly due to these limitations, the committee recommended in its second

report (NRC, 1999b) that a new type of screening process be used to identify

and evaluate a broader universe of microbiological, chemical, and other types of

potential drinking water contaminants in order to provide a more objective list

of contaminants of concern. In this regard, a conceptual two-step process for the

creation of future CCLs was outlined and recommended to EPA.

RELATED SDWA PROGRAMS

In accordance with the SDWA Amendments of 1996 and as indicated in

Figure 1–1, the development and use of future CCLs will be coordinated closely

with two other drinking water programs: the National Drinking Water

Contaminant Occurrence Database (NCOD) and the Unregulated Contaminant

Monitoring Regulation (UCMR) (EPA, 1998a). Both of these programs, as well

as the CCL, are the responsibility of EPA’s Office of Ground Water and

Drinking Water. EPA completed the first working release of the NCOD and the

UCMR prior to its amended SDWA statutory deadline of August 1999.

National Drinking Water Contaminant Occurrence Database

The NCOD stores data on the occurrence of both regulated and

unregulated drinking water contaminants. It is intended to support EPA efforts

in the identification and selection of contaminants for placement on future

CCLs; subsequent and related research, monitoring, and regulatory activities;

and the periodic (six-year) review of existing drinking water regulations for

possible modification as required under the amended SDWA (EPA, 2000g). An

additional purpose is to inform the public about contaminants detected in

drinking water and make available the data sets that help form the primary basis

for EPA’s drinking water-related regulatory and research actions. EPA

requested input from the

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

36

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
public, states, and the scientific community regarding the NCOD’s design,

structure, and use (AWWA, 1997; EPA, 1997c).

The first release of the NCOD became operational in August 1999 (as

mandated) and included occurrence data on various physical, chemical,

microbial, and radiological contaminants found in public water systems and

ambient (source) water (EPA, 2000g). More specifically, it contained some

summary statistics of PWS data stored in EPA’s Safe Drinking Water

Information System (SDWIS) and ambient water stored in the National Water

Information System (NWIS) of the U.S. Geological Survey (USGS). The

second release of NCOD became operational in late August 2000 and included

several changes intended to increase its functionality. For example, the second

release of NCOD completely refreshes summary PWS data every quarter (Lew

Summers, EPA, personal communication, 2000) and is supported by an NCOD

User’s Guide (EPA, 2000h).

At present, four types of NCOD queries have been defined: (1) public right

to know; (2) occurrence data survey for preliminary CCL8; (3) regulation

determination/development; and (4) regulation revision (EPA, 2000g). In brief,

the public right-to-know query allows a user to query the database for a specific

contaminant in specific political jurisdictions (i.e., national, state, county, or

city) grouped by population and source water type. The query on occurrence

data survey for preliminary CCL presents information on unregulated

contaminants in public drinking water from EPA’s SDWIS and ambient source

water from NWIS provided by USGS. A regulation determination/development

query is still under construction but will allow users to view summary

information and download PWS sample data that could be used to help select

contaminants for future regulation or to develop new national primary drinking

water regulations (NPDWRs). Last, the regulation revision query (also still

under construction) will allow users to view summary data and to download a

data set that could be used in the periodic revision of existing NPDWRs as

required under the amended SDWA.

Despite these ongoing improvements, EPA has provided (EPA, 2000g)

some self-assessed data limitations (and strengths) associated with the first and

second releases of the NCOD, along with several cautions to NCOD users,

including the following:

8 Note, the term “preliminary CCL” as used for this NCOD query differs substantially

from its use throughout this report.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

37

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
• The NCOD does not contain occurrence data from every public water

system or from every state. Only PWS occurrence data reported to the

SDWIS are available using a public water system query in NCOD.

• The NCOD contains data both for currently unregulated contaminants

required to be monitored by PWS under the UCMR (see more below) and

for the occurrence of regulated contaminants with health-based drinking

water MCLs. However, not all states and territories, or PWSs within states

and territories, have reported data for either type of contaminant data as yet.

Furthermore, the historical data goes back only to 1983.

• The NCOD contains occurrence monitoring data from sampling locations

throughout a PWS. However, detections do not necessarily mean that the

contaminant would be found at the tap.

• The NCOD contains ambient (source) water quality data from USGS for

river basins from 1991 to 1998. Ambient occurrence data are provided to

identify presence in a watershed; however, contaminant occurrence in the

ambient data does not imply that the contaminant is also present in a nearby

PWS.

• Although the NCOD data sets will be updated over time, they may still

reflect a lag time of at least six months from data provided directly from a

PWS. Thus, getting data that reflect the current conditions of a PWS is not

possible from NCOD.

• Reports generated by the use of EPA’s NCOD database are designed to

provide coarse summaries of the underlying data. The amount of

occurrence data already stored in the NCOD is quite large and is expected

to increase by as much as 1 gigabyte (1,000 megabytes) each quarter. This

could result in very large download files that could adversely affect the

performance of EPA public information servers. For this reason, NCOD

downloads are currently available only from reports generated by selecting

single contaminants.

Interested readers are encouraged to visit EPA’s Web site on the NCOD at

http://www.epa.gov/ncod/ for further information.

Unregulated Contaminant Monitoring Regulation

Section 125 of the 1996 SDWA Amendments requires EPA to

substantially revise the previous SDWA regulations for unregulated

contaminant monitoring (UCM; Title 40, Code of Federal Regulations, Part

141) (EPA, 1999g). The new program includes (1) development of a

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

38

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
new list of UCMR contaminants every five years; (2) a representative sample of

small public water systems serving 10,000 persons or fewer to conduct the

monitoring (in addition to all large systems); (3) placement of the monitoring

data in the NCOD; and (4) notification of consumers that monitoring results are

publicly available. The 1996 amendments also limit the number of unregulated

contaminants to be monitored by a PWS in any given period to a maximum of

30 and specify that EPA pay the reasonable costs of analyzing the samples

taken by those systems designated as “small.” EPA plans to use data generated

by the UCMR to (1) evaluate and rank contaminants on the 1998 CCL and help

develop future CCLs; (2) support its determinations of whether to regulate a

contaminant under the drinking water program; and (3) support the

development of drinking water regulations. The final UCMR rule will replace

almost all of the existing monitoring requirements of the existing UCM rule

when it takes effect on January 1, 2001.

In accordance with recommendations from a previous report (EPA, 1997d)

on options for developing the UCMR, EPA used the original occurrence

priorities of the 1998 CCL (as published on March 2, 1998) as the primary basis

for selecting contaminants for future monitoring under the UCMR (EPA,

1999f,g). These 26 chemical and 8 microbiological contaminants9 were then

evaluated primarily for the analytical methods and the level of information

available for them at the time of development of the 1999 UCMR List (see

Table 1–4 for a current alphabetical listing). Based on these evaluations, EPA

developed a three-tier monitoring approach that allows assessment monitoring

to start promptly for contaminants with approved analytical methods (UCMR

List 1), while accommodating the need to delay implementation for

contaminants requiring further refinement of analytical methods to initiate

screening survey monitoring (UCMR List 2) and those that need method

development for prescreen testing (UCMR List 3) (EPA, 1999c,f,g). Please

refer to the final UCMR rule (EPA, 1999f) for further information on

assessment monitoring for List 1 contaminants and the proposed rule for

screening survey monitoring for List 2 contaminants (EPA, 2000j).

In summary, List 1 contains 12 chemical contaminants that require

monitoring for one continuous year between 2001 and 2003 at all 2,744

9 It is important to note that the most recent occurrence priorities category of the 1998

CCL (see Table 1–3) includes two contaminants (Mycobacterim avium intercellulare and

1,3-dichloropropane) that were not listed in that category at the time the CCL was first

published and were thus not evaluated or listed on the 1999 UCMR List.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

39

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

40

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
large PWSs serving more than 10,000 persons and at a representative

national sample of 800 (out of 66,000) small systems serving 10,000 or fewer

persons (EPA, 1999f,g). Monitoring for the contaminants on UCMR Lists 2 and

3 is not required until EPA promulgates revisions to the UCMR final rule that

specifies analytical methods and related sampling requirements. However, EPA

recently revised List 2 and published proposed analytical methods and

monitoring requirements for 14 (13 chemicals and 1 microorganism) of its now

16 contaminants (see Table 1–4; EPA, 2000j). More specifically, EPA is

proposing to require screening survey monitoring for these 13 chemical

contaminants at 180 randomly selected small systems with the small systems

doing the sampling and EPA conducting the testing and reporting beginning in

January 2001. A total of 120 randomly selected large systems would begin List

2 chemical monitoring in January 2002. A second (delayed) screening survey

for Aeromonas will be performed in 2003 by 180 other small systems and 120

other large systems. The delay is intended to allow laboratories to gain

experience with the new method and develop the capacity for testing in large

systems. More generally, evaluation of the newly proposed methods during

screening survey monitoring for List 2 contaminants will include developing the

data necessary to support the

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

41

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
determination of practical quantitation levels, which are needed to support

possible future regulations, as well as determining the occurrence of the

analytes measured.

Notably, the addition of the majority of List 2 contaminants will not

require any PWS to monitor for more than 30 total unregulated contaminants

during the first five-year UCMR monitoring cycle, as specified in the amended

SDWA. At this time, EPA does not expect to publish analytical methods for

List 3 contaminants before the next UCMR is required in 2004 (Rachel Sakata,

EPA, personal communication, 2000). Therefore, it is likely that these

contaminants will be retained on the 2003 CCL (at least as occurrence

priorities) and the 2004 UCMR and will be monitored accordingly. In this

regard, the 2004 UCMR is expected to use the occurrence priorities from the

next (2003) CCL as the primary basis for selecting contaminants for future

monitoring under the UCMR (EPA, 1999d).

IDENTIFYING AND SELECTING CONTAMINANTS FOR

FUTURE CCLS

As noted earlier, the committee produced this report at the request of EPA

to build upon, expand, and revise as necessary the conceptual approach

recommended in its second report (NRC, 1999b). In that report, the committee

stated that if resources were unlimited and health effects and occurrence

information were perfect, an ideal CCL development process would include the

following features:

• It would meet all statutory requirements of the SDWA Amendments of

1996, such as requirements for consultation with the scientific community

and opportunities for public comment.

• It would begin with identification of the entire universe of potential

drinking water contaminants prior to any attempts to rank or sort them.

• It would address risks from all potential routes of exposure to water

supplies, including dermal contact and inhalation as well as ingestion.

• It would use the same identification and selection process for chemical,

microbial, and all other types of the potential drinking water contaminants.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

42

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
• It would use mechanisms for identifying similarities among contaminants

and contaminant classes to assess the potential risks of individual

contaminants—especially emerging contaminants.

• It would result in CCLs containing only contaminants that when regulated

would reduce disease, disability, and death, and excluding contaminants

that have few or no adverse effects on human health (e.g., contaminants

removed or detoxified through conventional drinking water treatment

methods).

However, EPA’s resources are still constrained; no comprehensive list of

potential drinking water contaminants yet exists; and health effects, occurrence,

and other related data for the vast majority of potential contaminants are poor or

nonexistent (NRC, 1999b). Despite these limitations, the committee continues

to recommend that EPA develop and use a two-step process for creating future

CCLs as illustrated in Figure 1–3. In brief, a broad universe of potential

drinking water contaminants

FIGURE 1–3 Recommended two-step process for developing future CCLs.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

43

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
is examined and narrowed to a preliminary CCL using simple screening criteria

and expert judgment. All PCCL contaminants are next individually assessed

using a “prototype” classification tool in conjunction with expert judgment to

create the corresponding (and much smaller) CCL.

The committee also continues to recommend that this two-step process be

repeated for each CCL development cycle to account for new data and potential

contaminants that inevitably arise over time. In addition, all contaminants that

have not been regulated or removed from the existing CCL should be retained

automatically on each subsequent CCL. Chapters 3 to 5 of this report provide

detailed descriptions, examples, and recommendations for implementing the

recommended two-step approach.

PERSPECTIVE OF THIS REPORT

As its previous reports, the promotion of public health remains the guiding

principle of the committee’s recommendations and conclusions in this report.

As in those documents, the committee recognizes that questions of economic

impact, required technological shifts, and political orientations and concerns are

all related to how safe drinking water is provided and at what cost. However,

the committee has adopted an explicitly public health orientation because

section 1412(b)(1)(A)(i) of the amended SDWA specifically directs the EPA

administrator to identify “contaminants for listing [that], first, may have an

adverse effect on the health of persons.” Further, section 1412(b)(1)(c) specifies

that EPA must focus on contaminants that pose the “greatest health concern.”

Therefore, in framing this report the committee has chosen to adopt an explicit

public health perspective, rather than any of a number of other possible

perspectives (e.g., enterprise centered, economic development, legal). The

report should be read with this qualification in mind.

DRINKING WATER CONTAMINANT CANDIDATE LIST: PAST, PRESENT, AND

FUTURE

44

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
2

Sociopolitical Considerations for

Developing Future CCLs

INTRODUCTION

In its two previous reports (NRC, 1999a,b), the committee emphasized that

the decisions related to the development of a Drinking Water Contaminant

Candidate List (CCL), as well as in the prioritization of CCL contaminants for

subsequent regulatory or research activities, must be scientifically defensible

and transparent. In Chapter 1, the development and implementation status of the

first CCL and two related programs are described briefly. This chapter discusses

the nature of the perennial task facing the U.S. Environmental Protection

Agency (EPA) in developing and implementing a decision-making process for

the creation of future CCLs in conjunction with several important social and

political issues. More specifically, the interrelated issues of sound science, risk

perception, social equity, legal mandates to consider the risks for vulnerable

populations, and the proper role of transparency and public participation are

discussed. In addition, several potentially helpful conceptual frameworks for

risk perception and public participation are described. In so doing, the

committee seeks to provide a solid foundation and background support for a

systematic, scientifically sound, transparent, and equitable approach to the

development of future CCLs, which is fully described later in this report.

LIMITATIONS OF THE FIRST CCL DEVELOPMENT

PROCESS

Because of the severe time constraints stipulated by the Safe Drinking

Water Act (SDWA) Amendments of 1996 for publication of the first CCL, EPA

was forced to develop and utilize a decision-making process for the creation of

the 1998 CCL that the committee feels was not sufficiently

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 45

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
defensible, transparent, or available for public comment. In particular, several

major policy decisions were made during the process that lacked sufficient

explanation and justification. By identifying and thoroughly discussing these

limitations, the committee aims to illustrate how important it is to arrive at a

more systematic CCL development mechanism that directly addresses these and

other sociopolitical issues. The committee notes that its second report (NRC,

1999b) also describes (albeit summarily) some limitations of the first CCL

development process.

A major policy decision made during the development of the first CCL

was to use completely separate approaches to evaluate potential chemical and

microbiological drinking water contaminants. The committee believes that the

justification given by EPA (EPA, 1997a) for conducting two independent

assessments was not adequate and perpetuates the long-established and often

unnecessary regulatory practice of treating chemical and microbial drinking

water contaminants separately and differently. As reviewed in the committee’s

first report (NRC, 1999a) and in more detail in Chapter 6 of this report, rather

than regulating each type of microorganism to a specific concentration as done

for chemicals, regulators historically have established a “zero-tolerance” goal

for microbiological contaminants. Indicator organisms, particularly fecal

coliforms, are then used to show the possible presence of microbial

contamination resulting from human waste. While this approach has served well

for indicating widespread sewage contamination of surface waters and for

controlling diseases such as cholera and typhoid fever, several deficiencies in

this approach have come to light in recent decades. For example, some bacteria

and many viruses and protozoa show greater resistance to conventional

treatment methods than do fecal coliforms. This approach has also led to a

deficiency of occurrence databases for microbial contaminants. The committee

continues to believe that the time is rapidly approaching when the same risk

assessment principles should be applied to the management of microbial

contaminants as are applied to chemical contaminants. Indeed, as described in

Chapter 1 and elsewhere in this report, the committee recommends a two-step

approach for the development of future CCLs that will similarly assess

chemical, microbial, and other types of potential drinking water contaminants.

Second, all potential chemical drinking water contaminants that were

considered initially for inclusion on the CCL were taken directly from existing

databases and lists of chemicals produced by various regulatory programs

within EPA and by stakeholder groups (see Table 1–1; EPA, 1997a). Although

this was useful for developing a CCL in a short time

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 46

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
period, EPA excluded from consideration several tens of thousands of

chemicals that might pose a threat to safe drinking water but have not yet been

identified or included on one of the selected lists (NRC, 1999b). For example,

the first CCL development process did not evaluate radionuclides or

pharmaceuticals. Moreover, reliance on databases created primarily for

purposes other than supporting drinking water research and regulatory activities

led to the inclusion of chemicals (e.g., acetone) on the draft 1998 CCL that were

ultimately determined not to merit such attention and were subsequently

dropped. In addition, reliance on these existing lists and databases implies a

policy decision to focus the CCL solely on chemicals already identified by EPA

programs, increasing the likelihood that a newly emerging chemical threat

would be missed by the process unless it caused a major problem.

Although the chemical and microbial contaminants included on the 1998

CCL certainly merit regulatory and research attention, a broader approach to

contaminant selection could potentially identify higher-risk contaminants. In

short, the committee characterized this overall approach as “looking under the

lamp post” for a relatively few types and numbers of drinking water

contaminants compared to the universe of potential contaminants (NRC, 1999b;

see Chapter 3 for further information).

A third major policy decision was the exclusion of contaminants from

further consideration if their occurrence in drinking water was not first

determined to be demonstrated or anticipated based on available information

(EPA, 1997a). By evaluating occurrence before any evaluation of health effects

data, drinking water contaminants with potential adverse health effects would

be excluded from the 1998 CCL solely because of missing or inadequate data

on occurrence.

Fourth, EPA decided to defer consideration of 23 chemicals and chemical

groups for inclusion on the draft 1998 CCL based solely on the possibility of

their being endocrine disruptors (EPA, 1997a). The rationale for this policy

decision was that these types of chemicals were then under general review by

EPA’s Endocrine Disruptor Screening and Testing Advisory Committee and

another NRC committee. Similarly, a list of 35 pesticides that indicated a high

risk of leaching into groundwater but met no other criteria for inclusion on the

draft 1998 CCL were also deferred pending further evaluation by EPA’s Office

of Pesticide Programs for their potential to occur at levels of health concern.

Both of these policy decisions imply that research on the health effects of these

important chemicals and their occurrence in drinking water could be delayed

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 47

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
at least until the development of future CCLs.

Finally, the stipulated size of the CCL reflected an unstated policy decision

on the general amount of resources EPA would devote to the regulation of

drinking water contaminants. In this regard, a total of 262 chemicals and groups

of related chemicals were identified initially for consideration during

preparation of the 1998 draft CCL (EPA, 1997a). As noted in the committee’s

first report (NRC, 1999a) however, EPA made it clear that the total number of

contaminants on the draft and final 1998 CCLs would have to be reduced from

262. Ultimately, the final 1998 CCL included a total of 60 individual chemicals,

or groups of related chemicals, and microorganisms (EPA, 1998a). Thus, for

planning and discussion purposes, the committee has always considered that

future CCLs would similarly be limited to no more than 100 or so total

contaminants as noted elsewhere in this report. Indeed, the committee feels that

EPA’s difficulty in developing a highly complex research plan for the 1998

CCL (reviewed in Chapter 1) supports this contention.

The committee also notes that the entire 1998 CCL development process

allowed for only limited public participation. As noted in Chapter 1, EPA relied

heavily on the advice of the National Drinking Water Advisory Council

(NDWAC) Working Group on Occurrence and Contaminant Selection for the

development of the approach used to create the draft 1998 CCL. Although

NDWAC’s meetings are open to the public and include experts from various

stakeholder groups, it cannot be considered to represent the broad spectrum of

public views on this issue. Thus, for the most part, public input into the

development of the 1998 CCL was confined to the two-month public comment

period for the draft 1998 CCL.

The committee, several public commenters, and EPA recognized the need

for a more systematic, scientifically sound, and transparent process for selecting

contaminants for future CCLs (EPA, 1998a,c; NRC 1999a,b). Specific

comments indicative of the lack of transparency in the decision-making process

for the draft 1998 CCL included calls for clarification of the process as a whole

by EPA so that it could be more fully understood (EPA, 1998c). Requests were

also made to EPA for explanations and justifications of the screening criteria

used to narrow the field of candidate contaminants. Questions were raised as to

how these specific criteria were selected, defined, operationalized, and

weighted. Several other comments focused specifically on the health effects

criteria (e.g., whether carcinogenic effects received priority over other health

effects or whether priority was given to contaminants with more complete

toxicological data). Some commenters complained of

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 48

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
insufficient explanation and justification for the inclusion or exclusion of

specific contaminants.

To a large extent, the widespread recognition of these limitations helped

lead to the formation of this National Research Council (NRC) committee at the

request of EPA to advise it on developing regulatory and research priorities for

the 1998 CCL and the creation of subsequent CCLs. Thus, a detailed discussion

of these issues provides a necessary and appropriate foundation for much of this

report. The committee believes that any proposed CCL classification scheme

must directly incorporate the principles of transparency, scientific defensibility,

and equity if it is to be successful at the policy level.

USE OF SOUND SCIENCE IN FUTURE REGULATORY

DECISIONS

The CCL is intended to be a central element in EPA’s future regulatory

strategy for its drinking water program. Section 1412(b)(3)(A)(i) of the

amended SDWA requires EPA to use the “best available, peer-reviewed science

and supporting studies conducted in accordance with sound and objective

scientific practices...”. The use of peer-reviewed science will not, however,

guarantee agreement among all parties that might be affected by the listing of a

chemical or microorganism on a CCL since scientists often weigh the different

strands of evidence and supporting data differently. Indeed, the committee

explicitly noted in its preceding report that expert judgment must play a

substantial role in the process of developing a CCL (NRC, 1999b).

Furthermore, disagreements on some CCL listings are to be expected and do not

necessarily indicate that they are unsound. Rather, the soundness of the

judgments will have to be decided in the more-or-less usual way of reasoned

and supported argument among the contending and interested parties. This

question was dealt with in greater detail in the first report of the committee, and

its conclusions are repeated here (NRC, 1999a):

[The committee] takes the position that scientific disagreements are the norm

and do not signal a deviation from sound science. These disagreements may be

based on values other than strictly scientific ones, however, this does not mean

that the sides of the debate are not based on sound science. Indeed, it is not

unusual for scientists to disagree on the application of sound science to public

policy issues. Any scheme that

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 49

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
affects the provision of public water is likely to engender legitimate scientific

disagreement. The report also recognizes that identifying and agreeing on what

is sound science is itself a difficult and error-prone enterprise. It therefore

makes no recommendations on what “soundness” entails, letting the accepted

mechanisms of peer regard, peer review, and scientists’ habits of critical

thinking continue to serve as the ultimate arbiters.

NATURE OF THE TASK

The daunting task before the EPA and for which the committee is charged

to provide advice is how to take a large, unordered set of chemicals,

microorganisms, and other potential drinking water contaminants and separate it

into two sets:1 one (the CCL) to receive occurrence monitoring or research of

some sort preparatory to an eventual decision to regulate or not and another set

that will receive no regulatory or research attention aside from that otherwise

dictated by the advancing interests of science and commerce. This sorting of

contaminants into two discrete sets based upon available occurrence and health

effects attributes gleaned from the scientific literature is a classification problem.

More specifically, the committee’s objective is to recommend a

scientifically sound, transparent, and equitable process that can be used to

identify and cull from the universe of potential drinking water contaminants a

list (CCL) that contains primarily contaminants for which EPA may justify

expending considerable, albeit limited, resources to develop drinking water

regulations or to pursue occurrence monitoring or health effects, treatment, or

analytical methods research. This is the central idea behind the CCL as required

under the SDWA Amendments of 1996. In effect, this means that EPA is

probably limited to preparing a CCL that has perhaps up to 100 contaminants

and groups of related contaminants on it. This represents a full two-order-ofmagnitude reduction from a total universe of potential drinking water

contaminants that may number

1 A third set could be considered that includes those drinking water contaminants that

are (or can be) determined to present an urgent threat to public health. As noted in

Chapter 1, the amended SDWA specifically allows EPA to circumvent the CCL process

and issue interim regulations for such contaminants. Thus, they are not included in this

discussion and are not a major focus of this report.

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 50

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
in the tens of thousands given the number of chemicals currently in commercial

use.2 As noted in Chapter 1 (see Figure 1–3), the committee has previously

suggested (NRC, 1999b) and continues to suggest a preliminary step that

reduces the universe of potential contaminants to a smaller list of perhaps a few

thousand (the preliminary CCL or PCCL). Before the problem of reducing this

list to the CCL can be discussed; however, an overview of the difficulties of

conducting even a coarse screen to get to the PCCL is in order.

If one were to assume, as seems plausible, that only a very small

proportion of the universe of potential drinking water contaminants are likely to

be high-risk contaminants (e.g., of sufficient toxicity and likelihood to become

prevalent in drinking water), any screening of this list must be highly accurate

with respect to correctly identifying those contaminants that are not a problem.

Appendix A of the committee’s first report (NRC, 1999a) included a

quantitative analysis of this situation, many elements of which are summarized

below.

The arithmetic of culling a list containing perhaps tens of thousands of

potential drinking water contaminants to a much smaller, but still large, list of

“high-risk” contaminants quickly reveals some inherent dilemmas. Assuming

one could reduce such a list of contaminants down to a list of a few thousand

(the PCCL) with some accuracy (e.g., it would include only chemicals with a

genuine potential to occur in drinking water and cause adverse health effects in

exposed persons), this judgment must be made with considerable accuracy. If

one were to make an error in only one of a hundred contaminants, wrongly

believing it has potential for contaminating drinking water or that it has more

health significance than it actually does, almost a quarter of the contaminants on

a smaller list of around 2,000 (the PCCL) will be “false positives” (one of a

hundred contaminants on a list of about 50,000 produces 500 false positives).

Thus, a very high level of specificity is necessary to avoid cluttering the PCCL

with a high proportion of relatively harmless contaminants. (In this case, a

specificity of 99 percent is the proportion of harmless contaminants that are

correctly identified as harmless).

On the other hand, the requirement for a relatively high level of specificity

runs counter to the usual public health emphasis of acting in a health-protective

manner. As stated in both of its previous reports (NRC,

2 The total number of contaminants in this universe could be quite large, based on the

European experience (see Appendix A of this report) and given that the Toxic

Substances Control Act inventory of commercial chemicals alone includes about 72,000

substances (NRC, 1999b).

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 51

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
1999a,b), the committee continues to recommend that EPA “err on the side of

public health protection,” opting for high sensitivity in the generation of future

CCLs. However, the opposing requirement not to include false positives makes

it more likely that certain contaminants that may truly pose a health risk will be

passed over as well (i.e., false negatives are left behind while trying to avoid

false positives).

Another approach would be to proceed “from the other direction” by

starting at the final list (the CCL) and trying to populate it with contaminants

already known or reasonably suspected to be problematic on other grounds

(e.g., chemicals that already have health advisories or are included on some list

of environmental concern). Using this strategy, one would be ignoring the huge

bulk of contaminants for which little or no data have been accumulated. To a

large extent, the draft 1998 CCL was developed using just such an approach.

Yet the unwelcome “surprises” (e.g., methyl-t-butyl ether [MTBE]) invariably

arise from the large group of contaminants with little or no data. Perhaps an

argument could be made that this practice is acceptable since the high-risk

characteristics described should receive priority in any regulatory scheme and

EPA can circumvent the CCL process to develop interim regulations for any

contaminants that are determined to represent an urgent threat to public health

(EPA, 1998a). However, it is important to note that one of the goals of the CCL

—to avoid such surprises—would be compromised to some extent.

The unpleasant arithmetic properties of screening large numbers of

contaminants aside, one must also consider the difficulty of conducting any

classification task where imperfect and incomplete data must be used to answer

a sophisticated question: in this case, Does a chemical, microbiological, or other

type of contaminant pose an existing or future threat to drinking water supplies?

Because of its general importance, the task of classification has been the

subject of a great deal of research in recent years (see, for example, Bowker and

Star, 1999). One need only consider commonplace examples to see how

difficult and complex a task it can be. For example, applications to college are

made on a relatively standard data collection format, including readily

quantifiable scales such as SAT scores, grade point averages, and demographic

data; have little or no missing data; and have sufficient history and numbers to

make quantification and statistical investigations with adequate power possible.

Despite these strengths, the level of public confidence in the accuracy of the

classification procedure (i.e., admit-don’t admit) is not high, with an

understanding that many worthy applicants will be rejected and some relative

failures will be admitted.

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 52

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Perhaps worse, suspicion exists that students are admitted or rejected in a

nonrandom fashion although the underlying mechanisms are often hard to

discern.

To summarize, the placement of a contaminant first on the PCCL and from

there on the CCL involves not one, but several, difficult classification

judgments. For example, does the chemical produce a health effect? (“Health

effects” is a category.) What are the specific health effects? (Health effects are

categorized into different types or diseases that change periodically.) What is

the nature of the evidence? (Evidence is placed into several, often overlapping

categories such as animal studies, epidemiological studies, and “reliable”

studies.) What appears at first to be a “simple” classification exercise quickly

reveals itself to be a complex task in which many choices are being made, some

explicitly, some as a result of given prior classifications, and some implicitly or

without conscious knowledge of the classifiers.

RISK PERCEPTION

The selection of microorganisms, chemicals, and other types of

contaminants for inclusion on a CCL will be based on judgments of their

potential health risks, including an evaluation of the severity of their effects,

their potency, and the likelihood of their occurrence in drinking water. These

risk judgments must be made in a context of considerable scientific uncertainty

(e.g., due to data gaps and the use of models to estimate potency and

environmental fate), complexity (e.g., variability in the vulnerability of

subpopulations and the effects of contaminant mixtures), and controversy (e.g.,

issues concerning acceptable evidence, relevant data, and the fairness and

acceptability of risks). Faced with this context, people rely on their

assumptions, values, beliefs, and in general, their worldviews, as well as on the

information available to them (which to some extent is also dependent on their

worldviews) in order to make judgments about risks. Therefore it should not be

surprising that people differ in their perceptions of risk and that these

disagreements reflect differences in their worldviews.

A worldview can be defined as a deeply held “orienting disposition”

toward the world and its social organization that guides a person’s perceptions,

interpretations, analyses, and responses in a wide variety of complex situations

(Peters and Slovic, 1996). An individual’s worldview develops from his or her

life experiences, social interactions, and education.

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 53

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
It includes perceptions of self-identity and local environment (e.g., local

hazards, local socioeconomic conditions), political beliefs and moral values,

and views and values held by the social groups with which an individual

identifies and belongs. The prevailing views of one’s discipline, or the values of

the institution by which one is employed, may also be incorporated into one’s

worldview and influence how one perceives risks (see, for example, Barke and

Jenkins-Smith, 1993; Kraus et al., 1992; Slovic et al., 1995). Another important

aspect of an individual’s worldview is the primary reasoning scheme a person

uses in complex situations such as those involving the risk perception of an

environmental hazard.

Research over the past 30 years has emphasized the differences in risk

perception between scientists and “lay people” who are not professionally

trained in science (e.g., Sowby, 1965; Starr, 1969). According to this research,

scientists tend to adopt a quantitative approach to risk and emphasize

considerations such as dose and exposure. On the other hand, lay people

generally adopt a more qualitative approach, emphasizing the fairness and

voluntary nature of the risk, its effects on future generations, and the

characteristics of the risk such as whether it is known, uncontrollable, or

capable of producing catastrophic effects (Tesh, 1999). At the aggregate level,

surveys have found differences in average risk perception scores when scientists

as a group were compared with lay people as a group (Barke and Jenkins-Smith,

1993; Kraus et al., 1992; Slovic et al., 1995). However, these surveys also

found a wide spectrum of views on risk perception within each group indicating

that at the individual level, the risk perceptions of many scientists and lay

people may not differ substantially. For example, it is not unusual that when

confronted by complex environmental hazards, lay people often become selftaught, scientific experts who work closely with professional scientists to

evaluate the technical aspects of the risks (Brown, 1992, 1997; Tesh, 1999). On

the other hand, many scientists have attempted to achieve a balance between

quantitative and qualitative approaches to the evaluation of environmental risks.

The wide spectrum of views on risk perception found at the individual level

among scientists as well as among lay people reflects differences in worldviews

within these two heterogeneous groups.

Three schemes of reasoning can be differentiated theoretically and are

discussed below to aid understanding of risk perception (although an

individual’s primary reasoning scheme is likely to be some combination of

these) as related to the classification of drinking water contaminants for

regulatory consideration. In the “utilitarian” reasoning scheme, each

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 54

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
person is assumed to be a self-interested “utility calculator,” determining the

optimum balance of personal satisfaction from among various options and the

information available (Anderson, 1993). At the societal level, the goal is the

efficient and optimum distribution of costs and benefits so as to maximize

benefits for the majority of society’s members. In this scheme, risks would be

ranked on a common metric such as expected number of deaths per year, quality

adjusted life-years (QALYs), or disability adjusted life-years (DALYs), and this

metric would provide the basis for comparisons and cost-benefit trade-offs

among risks. This reasoning scheme tends to juxtapose widely different forms

of death (e.g., immediate death, death after a long and debilitating illness) as

well as voluntary and involuntary risks (Bennett, 1999). It is also generally

indifferent to issues of equity or social justice (Morrow and Bryant, 1995). In

the context of risk perception, this reasoning scheme tends to assume that some

level of risk is necessary for “growth” and therefore attempts to answer in a

quantitative, cost-benefit fashion the question: How safe is safe enough?

Policies associated with (but not necessarily logically entailed by) this

reasoning scheme tend to emphasize managing risks over risk avoidance and

avoiding false-positive errors (e.g., ameliorating a risk that later proves

harmless) over false-negative errors (e.g., failing to detect an important health

hazard).

A second reasoning scheme focuses on the interpretation, elaboration, and

assertion of rights (or entitlements to rights) and has been called “liberal

rationalism” (Anderson, 1993). Reciprocity (e.g., mutual tolerance, respect,

trust, goodwill) is the key value, and social justice is the goal of this approach.

In the context of environmental risk, this approach would be concerned with the

fair distribution of hazards among social groups and would attempt to answer

the following question: How fair is safe enough (Rayner and Cantor, 1987)? In

this scheme of reasoning, the “average person” does not exist; instead risks are

perceived as varying by social group. Policies addressing societal demands for

environmental justice, the protection of vulnerable subpopulations, and the right

to tap water that is safe to drink are based on this reasoning scheme.

A third reasoning scheme, called “critical reason,” is concerned with “bigpicture” issues such as a search for the common good, the essential purposes of

policies and activities, and the characteristics of the ideal society (Anderson,

1993). It involves thinking self-consciously about big-picture goals and

purposes and integrating the grounds for society’s basic values, beliefs,

practices, priorities, and institutions with one’s own values, beliefs, interests,

and activities. In this, ideals such as the goods

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 55

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
one should seek, the “good life,” social equality, “best practices,” and the

proper consideration and treatment of humans and the ecosystem are used as

evaluative standards. Presupposed by this reasoning scheme is a notion of

“deliberative democracy” in which members of an openminded community of

equals are willing to move beyond self-interest and attempt to reach consensus

on the general interest and common good (Einsiedel and Eastlick, 2000).

In the context of environmental risk, critical reasoning might ask whether a

risk is necessary or justifiable given the kind of society that is desired and

whether the decision about the acceptability of the risk was reached through an

open and democratic process. For example, regarding the development of

drinking water regulations, critical reasoning might ask whether the setting of a

maximum contaminant level protects the public from a risk or rather acts to

legitimize the risk. Policies emphasizing risk avoidance, toxic chemical use

reduction or elimination, concerns for future generations, and concerns for

community cohesion and participatory decision-making would be based on this

critical reasoning scheme.

As discussed in great detail in Chapter 4, future CCLs will entail the

evaluation of contaminant attributes such as the severity of potential health

effects, potency (dose-response relationship), occurrence in drinking water, and

the likelihood of resulting exposures. Each of the three reasoning schemes

would approach the evaluation of these attributes differently. For example, a

utilitarian approach to the severity of a contaminant’s potential health effects

might focus on effects that are easily measurable such as deaths, injuries, or

monetary damages in order to rank contaminants in a simple, direct fashion.

Although such an approach has the appearance of “neutrality,” hidden

assumptions and values concerning which health effects are considered more or

less severe are inherent in the choice of the metric. For example, embedded in

the choice of metric might be a bias for ranking prevalent diseases over rare

diseases, cancers over birth defects, chronic over acute diseases, and so forth.

Because these values and biases are implicit, they are not likely to be discussed.

A liberal rationalism approach might address the issue of severity by

comparing hazards in terms of the effects they have on the most susceptible

groups. Alternatively, the potential health effects occurring in one vulnerable

population (e.g., infants) might take precedence over the effects occurring in all

other subpopulations. Here the values and biases would be out in the open and

could be debated more easily.

A critical reasoning approach might explore what it is about certain

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 56

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
health effects that makes them perceived to be more severe than others. For

example, what is it about cancers that usually makes them appear more severe

than other health effects such as mood changes, depression, infertility, and

developmental disorders? The goals would be to facilitate deliberation on the

hidden, taken-for-granted values embedded in society’s concept of severity and

to reach consensus on a concept of severity that would lead to improvements in

disease prevention, protection of future generations, and protection of the

ecosystem. The critical reasoning approach might also explore how the severity

of an effect changes when one moves from an individual to a societal

perspective. Finally, such an approach might eschew the ranking of effects by

severity altogether and take the position that any hazard that is known or

suspected to cause an adverse effect in humans should be minimized or

eliminated.

PROTECTION OF VULNERABLE SUBPOPULATIONS

Section 1412(b)(1)(c) of the SDWA Amendments of 1996 specifically

requires EPA to give priority to selecting contaminants for inclusion on future

CCLs that present the greatest public health concern, taking into consideration

“...the effect of such contaminants upon subgroups that comprise a meaningful

portion of the general population (such as infants, children, pregnant women,

the elderly, individuals with a history of serious illness, or other

subpopulations) that are identifiable as being at greater risk of adverse health

effects due to exposure to contaminants in drinking water than the general

population.” In short, EPA must act protectively to ensure that vulnerable

subpopulations receive safe drinking water. In this way, the goal of providing

safe drinking water to all consumers can be achieved.

The committee recommends that the list of vulnerable subpopulations

described in the amended SDWA should not be seen as a minimum list, but

rather as several examples of possible vulnerable subpopulations. A minimum

list must go much further than this. In this regard, EPA should consider

including other subpopulations that are potentially vulnerable, such as all

women of childbearing age, fetuses, the immunocompromised, people whose

genetic disposition makes them more vulnerable to drinking water

contaminants, people who are exceptionally sensitive to an array of chemical

contaminants, people with specific medical conditions that make them more

susceptible, and people with poor nutrition. As scientific knowledge about the

determinants of susceptibility

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 57

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
expands, our ability to identify vulnerable subpopulations will improve.

It should also be recognized that all people experience changes in

susceptibility and risk over time. For example, fetuses and the elderly are

recognized as exceptionally susceptible to a variety of chemicals and

microorganisms. All people experience hormonal changes during puberty that

may lead to increased susceptibility to certain chemicals (Golub, 2000). Most

women bear children at some point in their lives, and pregnancy is a time of

exceptional sensitivity to a wide variety of environmental contaminants

(Selevan et al., 2000). As our population ages and medical science becomes

increasingly more able to treat diseases that were once fatal, the proportion of

people who are especially susceptible to chemical and microbial exposures

continues to grow. For example, the most rapidly growing demographic group

in the United States is individuals over age 85 (FIFARS, 2000). Individuals

with AIDS, diabetes, and severe heart disease are now living many years longer

than in the past. Formerly incurable malignancies are now treatable, although

with medical approaches that often suppress immunity and thus render

individuals more susceptible to environmental contaminants. For these and

similar reasons, it should be understood that the legal mandate to protect

vulnerable subpopulations is not a mandate to provide special protection to

specific groups, but rather a directive to provide the broadest public health

protection to society as a whole.

The need to protect vulnerable subpopulations is not only legally mandated

by the amended SDWA, but also justified on equity and environmental justice

grounds. Subpopulations experiencing socioeconomic hardships and racial or

ethnic discrimination tend in general to have poorer health and poor access to

adequate health care—factors that can make them more susceptible to drinking

water contaminants (Brown et al., 2000; IOM, 1999). In addition, conditions

that can make a person more susceptible to drinking water contaminants, such

as HIV infection, are also more prevalent in these subpopulations. Finally,

many of these subpopulations live in areas that might be at higher risk of

exposure to toxic chemicals through air, soil, and drinking water. For example,

in the southern portion of the United States, a large proportion of the

populations experiencing socioeconomic hardships and racial or ethnic

discrimination lives in rural areas served by small water utilities. In general,

small water utilities tend to violate the provisions of the SDWA more often than

large systems and are less stringently regulated than the large systems (EPA,

1999b). In addition, many of the toxic waste sites in the South are located in

rural areas and could pose a threat to the

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 58

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
groundwater supplies used by small water utilities (Bullard, 1994).

The mandate to protect vulnerable subpopulations has implications for the

evaluation of chemical and microbial contaminant attributes such as the severity

of their potential health effects and their occurrence in drinking water (see

Chapter 4). For example, severity would not be assessed solely by its

prevalence in the general population or by its effect on the “average person,”

but would also be judged by its prevalence in vulnerable subpopulations. In the

evaluation of occurrence, the frequency of detection in space and time may be

less important than the locations at which a contaminant is detected (e.g., rural

areas, American Indian reservations).

At a recent expert workshop focused on susceptibility in microbial

pathogen risk assessment (Balbus et al., 2000), consensus was achieved on the

need to better define the agents and health outcomes associated with

“susceptibility.” Some particularly relevant questions raised and discussed

included the following:

• Are there specific characteristics of a pathogen that make it more or less

hazardous?

• What health effects should be considered as outcomes? Should only

adverse health effects be considered?

• Should a change in an individual’s immune state always be considered a

positive outcome or should it also be viewed as “adverse” if it places

people at risk for later, more serious and/or chronic health effects? When

microbial pathogens are the concern, should immunity be considered a

lifelong or a temporary state?

• Should susceptibility be treated as a dichotomous or a continuous variable

(e.g., susceptible or not versus an individual’s probability of response)?

Should susceptibility be framed as the population’s distribution of

individual probabilities of a specific response to a specific agent?

• Should a “susceptible subpopulation” be considered to include people at the

upper end of the population’s probability distribution (e.g., 95th or 99th

percentile) of response to pathogens or all people above the population’s

average response (i.e., greater than 50 percent likelihood of response)?

Workshop participants concluded that these and other issues must be

addressed on a pathogen-specific basis.

Therefore, the concepts and values that underlie the meaning of

“susceptibility” have to be clarified before such groups can be appropriately

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 59

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
identified and protected (Parkin et al., 2000). As noted above, susceptibility can

be defined either on the individual or the population scale. Thus, an updated

working consensus of “susceptible” will be needed to identify and protect

vulnerable subpopulations in accordance with the amended SDWA and in the

creation of future CCLs. Without this clarification, different stakeholders are

likely to approach the term with different conceptual frameworks regarding

their perceptions of risk. As a consequence, they may have conflicting but

unrecognized differences about what persons and groups should be considered

eligible for inclusion in such subpopulations. For example, are susceptible

people those who are at elevated risk because of exposure or because of an

inherent, nonmodifiable trait? Are risk and susceptibility conceived as related

primarily to a contaminant’s inherent characteristics (e.g., chemical structure or

virulence of a waterborne pathogen), the host’s immune status, or some

characteristic of the subpopulation itself? Until such issues are resolved, any

one definition of susceptible is likely to have important public policy

consequences because it may not necessarily address all people who need to be

protected from the adverse health events associated with all contaminants.

However, the committee emphasizes that none of these questions or the

important issue of what “meaningful portion of the general population” actually

means under the amended SDWA can be answered based solely on scientific

findings. Rather, the answers will depend in part on societal values and on

viable, democratic means of resolution. In this regard, the resulting deliberative

process may require several iterations before a working consensus emerges

(Franz and Jin, 1995; Malone, 1994). Strong and widespread social support will

be important in implementing effective programs to ensure safe drinking water

for vulnerable subpopulations.

TRANSPARENCY AND PUBLIC PARTICIPATION

Increasing public concern and activism on environmental issues have

resulted in demands that federal, state, and local regulatory and enforcement

policies be made more transparent and incorporate public participation. For

example, one of the key demands of environmental justice advocates is “the

right to participate as equal partners at every level of decision-making including

needs assessment, planning, implementation, enforcement and evaluation.”

(Gibbs and CCHW, 1995) To respond adequately to such legitimate demands,

the decision-making process for

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 60

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
developing future CCLs should include an oversight mechanism to allow public

participation in all aspects of its design, development, and implementation. The

process would also have to be transparent (i.e., “...easily understood, where

information about the policy is available, where accountability is clear, and

where citizens know what role they play in the implementation of the policy”;

Finkelstein, 2000)

Transparency

Fundamental to the notion of transparency are the principles of equity,

fairness, and democracy. The transparency of a decision-making process may

help to ensure that resulting decisions are not perceived as having been made

capriciously or “behind closed doors.” Yet how can the transparency of a policy

or decision-making process be evaluated when there is no consensus standard to

measure it? To this end, the committee recommends a recently proposed

information communication standard for risk assessment that could have

broader applicability as a general standard for transparency in decision-making.

This standard can be summarized as providing sufficient information on the

decision-making process such that citizens are allowed “...the opportunity to

place themselves in a similar position as the [decision-maker]...to make as

informed a choice...as if they themselves had gone through the [decisionmaking] process...” (Hattis and Anderson, 1999). In other words, one of EPA’s

major goals in developing future CCLs should be to explain the process

sufficiently so that with the information supplied an informed citizen could

arrive at their own reasonable and informed judgments. To meet such a standard

would require that transparency be incorporated into the design and

development of the decision-making process (and any models used in the

process) in addition to being an integral component in communicating the

details of the decision-making process to the public.

The issue and importance of transparency were raised at a November 1999

stakeholder meeting to discuss the implementation status of the 1998 CCL.

Notably, EPA pledged that it would produce as part of the forthcoming CCL

regulatory determination process a detailed support document that would

describe comprehensively the rationale for all decisions to regulate (or not)

CCL contaminants and provide a review of all the data used to support such

decisions (Michael Osinski, EPA, personal communication, 2000).

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 61

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
EPA has previously attempted to address the issue of transparency in its

Guidance for Risk Characterization (EPA, 1995). In an accompanying memo,

the former EPA administrator noted that stakeholders in environmental issues

desire sufficient information to allow independent assessments and judgments

about the significance of environmental risks and the reasonableness of EPA’s

corresponding risk reduction actions. The administrator stated that “we must

adopt as values transparency in our decision-making process and clarity in

communication with each other and the public regarding environmental risk and

the uncertainties associated with our assessments of environmental risk.” This

entails a full and open characterization of risks. The EPA administrator added

that by doing so, EPA would have to disclose the key scientific analyses and

policy choices, uncertainties, and core assumptions that underlie its decisions.

Furthermore, the guidelines state that achieving transparency in risk

characterization would require a frank and open discussion of the uncertainty

associated with an assessment as a whole and its components along with the

impacts of key factors or variables on the overall decision-making process

(EPA, 1995). They acknowledge that information from different sources carries

different kinds of uncertainty and that understanding these differences is

important when uncertainties are combined. The guidelines reassure risk

assessors and managers that a frank and comprehensive discussion of

uncertainties and their impacts on the decision-making process would not

necessarily reduce the public’s perceived validity of the process, but instead

would likely enhance public trust and serve as a useful indicator of the

confidence decision-makers had in the process itself and in resulting decisions.

Although not mentioned in the guidelines, an added benefit of such a discussion

of uncertainties is that “it helps the [decision-maker] function more honestly in

a context that may often exert pressures for more unambiguous answers than

can readily be produced” (Hattis and Anderson, 1999).

A recent evaluation of the use of models in an environmental assessment

effort called the “ULYSSES Project” in Europe has similarly reported that a full

discussion of uncertainties could promote public trust and enhance the

credibility of the assessment (Dahinden et al., 1999). However, the authors

warned that a full discussion of uncertainties could also produce public doubts

and skepticism about the results of the assessment. In this regard, Yearley

(1999) and Lopez and Gonzalez (1996) found that how the public receives a

presentation of uncertainties is most influenced by its existing “lay knowledge”

of a particular issue, its history of experience and participation with a related

regulatory agency, and

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 62

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
the resulting accumulation (or deterioration) of trust and credibility concerning

the agency’s agenda, policies, and decisions.

An important question for EPA in developing future CCLs is, How much

information should be provided to the public in order to effectively characterize

the uncertainties in the decision-making process? The 1996 NRC report

Understanding Risk: Informing Decisions in a Democratic Society warns that

“simple characterizations are likely to give an erroneous impression of the

extent of uncertainty, but more careful and elaborate characterizations may be

incomprehensible to nonspecialists...”. Thus, the goal would be to characterize

the uncertainties in such a way that citizens would understand the level of

uncertainty in the process and “...appreciate where scientists agree and where

they disagree.” In this regard, the committee again maintains that such

disagreements are normal and expected and do not necessarily signal a

deviation from the application of sound science.

As discussed later in greater detail (Chapters 3 to 5), this report

recommends and outlines a type of triage approach to move forward from a

universe of potential drinking water contaminants to arrive ultimately at a much

shorter list (the CCL) that will largely form the basis of EPA’s future drinking

water program. A major aspect of this triage approach will involve the

development and use of a “prototype” classification tool that may have a similar

degree of complexity and uncertainty as models that have previously been

utilized for the purposes of environmental risk assessment (e.g., those employed

in the ULYSSES Project). Therefore, the committee believes that in the

implementation of this recommended approach, EPA will have to deal with the

same issues of transparency and the full discussion of uncertainties that have

emerged from the use of these environmental risk assessment models.

The proposed classification tool is similar in concept to those already used

in medicine for clinical diagnoses of a wide variety of illnesses from

appendicitis to myocardial infarction (Baxt, 1995). Classification models have

also been used successfully in marketing and financial contexts to sort people

into various consumer niches or credit risk groups; in security contexts for

fingerprint, speech, and face recognition; and in weather forecasting. Although

the recommended classification model approach is innovative and indicates a

willingness to adopt techniques successfully employed elsewhere, the

committee cautions that it may run the risk of being viewed by the public in a

“conspiratorial” fashion. For example, the public may wonder whether such a

modeling approach is merely “a vehicle to prove what we think we already

know” or whether it

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 63

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
represents “an honest attempt to find answers that are not predetermined”

(Oreskes, 1998). In addition, the public could perceive the process as subject to

manipulation to achieve or support results wanted in the first place. Finally, the

process could be perceived as simply a means to remove accountability from

decision-makers by placing it on an “objective” modeling process (Yearley,

1999).

As noted previously, to gain widespread credibility and acceptance, the

CCL decision-making process must be transparent. It is important to note,

however, that transparency is not necessarily synonymous with simplicity. A

process that appears on the surface to be simple and easy to understand may in

fact be riddled with hidden assumptions. In contrast, it is possible for a CCL

decision-making process that involves complex classification modeling to be

made relatively transparent. This will require that transparency be integral to its

design, and EPA must be ready and able to support each step in the process as if

the process itself were on trial.

First, the use of a classification tool needs justification. The successful

experience of using such tools in other contexts and applications would help

make the case for its use in sorting various potential drinking water

contaminants. In addition, referring to the use of the tool in contexts that are

familiar in the day-to-day experience of the public (e.g., marketing and credit

profiling) could enhance public understanding of the process (Dahinden et al.,

1999).

Second, the methodology for designing and calibrating the decisionmaking process must be defended. This important issue and related

considerations are discussed in depth in Chapter 5. To this end, the committee

recommends that EPA make clear that the design of the process was in part

based on a “retrospective” approach whereby EPA starts with a scenario it

wants to end up with (e.g., a scenario that builds on past correct EPA decisions

to regulate or not to regulate certain chemicals) and then calibrates the model

based on this scenario. The results of citizen focus groups conducted in Europe

(for the ULYSSES Project) indicated that this retrospective type of approach to

model development would be acceptable to the public (Dahinden et al., 1999).

However, the choice of a scenario upon which to calibrate the model may not

be a trivial, non-controversial one and therefore may have to be vigorously

defended. In addition, disputes may occur (e.g., among decision-makers and

between decision-makers and the public) regarding the issue of what minimum

levels of sensitivity and specificity the model must have in order to be judged

“adequate.” Some justification must also be made for the selection of the key

parameters of the model (i.e., measures or attributes of

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 64

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
potential health effects and occurrence), as well as a discussion of the

magnitude of the effect each chosen parameter has on the model’s results and

the quality of data for each parameter (e.g., uncertainties, data gaps, timeliness

of the data). The choice of parameters must not be based simply on the fact that

quantitative data are available for them, in other words: “There is a human

tendency to count what is easy to count, and then, as a kind of ‘doublethink,’ to

mistake what is counted for what counts” (Hattis and Anderson, 1999).

Third, if decision-making for including or excluding certain contaminants

on future CCLs will ultimately depend on a combination of EPA judgment and

the results of a classification tool (as recommended in this report), then this

relationship must be fully articulated along with the background assumptions

underlying agency judgments. Furthermore, EPA will have to justify how the

consistency and explicitness of its decisions can be maintained in these

situations. Other related issues, such as to what extent political (including

budgetary) contingencies will affect EPA’s decisions in conjunction with

scientific and efficiency considerations (e.g., regarding the size of future CCLs),

will also have to be fully aired.

Fourth, it can be expected that some decisions to include or exclude

specific contaminants from the CCL will be controversial. For example, EPA

received and responded on a contaminant-by-contaminant basis to public

comments that opposed the inclusion of several contaminants and groups of

related contaminants (e.g., aluminum, organotins) on the draft 1998 CCL (EPA,

1998a,c). Thus, to help ensure transparency and legitimacy in the development

of future CCLs, any key criteria, data, or assumptions that ultimately made the

difference between inclusion or exclusion in such cases must be clearly

identified and carefully justified.

Although a classification model approach is recommended for the creation

of future CCLs, the committee notes it may also be worthwhile to use the tool to

aid thinking on the issue of contaminant selection for regulation and research.

For example, if the use of the tool produces unexpected results, it will force

decision-makers to review the assumptions, parameter choices, and

uncertainties incorporated in the model as well as how the tool works (Ravetz,

1999). Conducting a review of this kind might advance knowledge on the

science of decision-making as well as the science involved in the regulation of

drinking water contaminants. In addition, if such a review were discussed fully

in a public forum, the transparency of the model would also be enhanced (i.e., it

would look less like a “black box” mysteriously churning out results).

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 65

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Two additional ways to enhance the transparency of the classification

model would be (1) to remove or add parameters to determine how the selection

of contaminants changes (a discussion of such results would also serve to

improve the transparency of the process) and (2) to make the software and

databases used to design, develop, and implement the model available to the

public so that citizens can attempt to go through or recreate the process

themselves. However, the committee realizes that the latter may be impractical.

To conclude this discussion, Hattis and Anderson (1999) provide an

appropriate quote summarizing the importance of transparency in decisionmaking:

...We quantifiers will only succeed in being helpful to democratic decisionmaking if our work is designed and presented in such a way that it helps

affirm, rather than supplant, the decision-making autonomy of our audience.

Public Participation

“Public participation encompasses a group of procedures designed to

consult, involve, and inform the public to allow those affected by a decision to

have an input into that decision” (Rowe and Frewer, 2000). Furthermore, Renn

and colleagues (1993) noted that the central tenet of public participation is that

the public is, in principle, capable of making wise and prudent decisions.

The 1996 NRC report Understanding Risk: Informing Decisions in a 

Democratic Society adopts this central tenet and is critical of agencies that rely

on a “decide-announce-defend” strategy that involves the public only after the

deliberation process is over (NRC, 1996). However, the report uses the term

“deliberation” rather than “participation” to emphasize the need for substantive

public involvement throughout the decision-making process (Chess, 2000; NRC

1996). The report discusses at length the importance of “getting the right

participation” (i.e., sufficiently broad participation that includes the range of

interested and affected parties) and “getting the participation right” (e.g.,

incorporating public values, viewpoints, and preferences into the process).

Public participation procedures vary from one-way flows of information

(e.g., surveys, focus groups, public comment), where the aim is to elicit public

opinions, to collaborative forms of decision-making such as negotiated rule

making (Fiorino, 1990; Laird, 1993), consensus conferences

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 66

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
(Einsiedel and Eastlick, 2000), citizen juries (Lenaghan et al., 1996), and citizen

panels (Renn et al., 1993), where the aim is to elicit decisions and judgments

that will form the basis for actual policies (Beierle, 1998). A brief description of

several public participation procedures and evaluative criteria to assess their

strengths and weaknesses has been proposed by Rowe and Frewer (2000). Not

surprisingly, there is no procedure that is preferred in all situations (NRC,

1996). However, an effective public participation procedure might entail a

hybrid or combination of procedures such as the three-step procedure proposed

by Renn et al. (1993). This approach attempts to integrate (1) technical

expertise, (2) values and concerns of stakeholders, and (3) common sense and

personal experience of the general public to balance the legitimate demand for

public participation with the needs for a scientifically sound policy and agency

accountability.

In a given situation, the most effective procedure will depend, among other

factors, on the aims and rationale for participation. Regarding the development

of future CCLs, at least three rationales can be used to justify increased public

participation: political, normative, and epistemic (Perhac, 1998). Each rationale

leads to a different definition of who should participate on behalf of the

“public,” and this in turn affects the choice of participation procedure.

The political rationale is that public participation enhances the political

viability, legitimacy, and transparency of the process as well as the credibility

of the regulators. It is the recognition that even the most scientifically sound

process will be difficult to implement if it is perceived by the public as unfair or

biased. Using this rationale, the “public” would be defined as those people

whose acceptance is crucial for the viability and legitimacy of the process, such

as representatives of specific stakeholder groups. For example, negotiated rule

making is a public participation procedure that corresponds to this definition of

public.

The normative rationale appeals to democratic principles and holds that the

public has a right of involvement since it is the “owner” of publicly funded

regulatory policies and the most appropriate source of the value judgments that

are necessary in any decision-making process. This rationale assumes that all

are affected by regulatory policies and therefore all have the right to participate,

not just representatives of stakeholder groups. Under this rationale, a public

participation procedure should ensure that all citizens’ values and preferences

are fairly represented—regardless of whether these citizens are organized—and

that the output of the procedure (e.g., decisions or recommendations) has a

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 67

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
genuine impact on policy. Public participation procedures that follow from this

rationale would include referenda, public opinion surveys, focus groups,

consensus conferences, and citizen juries.

Lastly, the epistemic rationale recognizes that the public possesses

important factual knowledge (e.g., local knowledge) as well as special insight

on societal values and therefore public participation will result in better

decisions. Under this rationale, the “public” might be defined as those people

who possess special insight on values or unique factual knowledge that is

relevant to a given decision-making process. A procedure that follows from this

definition of the public would be citizen advisory committees.

General recommendations to facilitate public participation in

environmental programs are provided in The Model Plan for Public

Participation, developed for the EPA by the Public Participation and

Accountability Subcommittee of the National Environmental Justice Advisory

Council (EPA, 1996c). Hampton (1999) provides recommendations to ensure

that public participation procedures satisfy the criteria of equity, fairness, and

justice. Among these are the following:

• The public should be involved in defining the process of participation.

• Public involvement should start early in the process (e.g., at the time of

agenda setting or when value judgments become important to the process).

• Participants should have access to appropriate resources such as the

information necessary to participate fully in the process, access to

scientists, technical assistance, and sufficient time to prepare for the

deliberations.

• Prior agreement should be reached with the participants as to how the

output of the procedure (e.g., recommendations, decisions) will be used and

how it will affect agency policy decisions.

The NRC Committee on Risk Characterization concluded in its 1996

report, Understanding Risk: Informing Decisions in a Democratic Society, that

it was not possible to predict which public participation procedure would work

most effectively in any given situation, and this committee concurs. Each

procedure has its advantages and limitations, but a successful outcome will

usually depend less on the inherent aspects of a procedure and more on other

factors such as the history of an issue, the level of conflict, the level of public

trust in the agency, the agency’s

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 68

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
intentions and expectations for the selected procedure, and how well the agency

implements the procedure (Chess, 2000; NRC, 1996).

SUMMARY: CONCLUSIONS AND RECOMMENDATIONS

The committee recognizes that the development of a PCCL from the

universe of potential drinking water contaminants, as well as a contaminant’s

movement from a PCCL to the corresponding CCL, is a complex task requiring

numerous difficult classification judgments in a context where data are often

uncertain or missing. The evaluation of contaminant attributes, such as severity

of health effects, potency, and occurrence in drinking water (see Chapter 4),

will often entail making assumptions because of data gaps and uncertainties.

Moreover, evaluation of the severity of health effect and making comparisons

of severity among different health effects (e.g., cancers versus impotence) will

depend on explicit and implicit value judgments as well as on the choice of

reasoning scheme (e.g., utilitarian, quantitative). Because of this complexity,

the committee believes that to be scientifically sound as well as publicly

acceptable, the process for developing future CCLs must depart considerably

from the process used to develop the first (1998) CCL. The committee

recommends that the process for selecting contaminants for future CCLs be

systematic, scientifically sound, and transparent. The development and

implementation of the process should involve sufficiently broad public

participation. The ultimate goal of the contaminant selection process is the

protection of public health through the provision of safe drinking water to all

consumers. To meet this goal, the selection process must place high priority on

the protection of vulnerable subpopulations.

More specifically, the committee makes the following recommendations:

• The definition of vulnerable subpopulations not only should comply with

the amended language of the SDWA, but also should be sufficiently broad

to protect public health; in particular, EPA should consider including (in

addition to those subgroups mentioned as examples in the amended

SDWA) all women of childbearing age, fetuses, the immuno-compromised,

people with an acquired or inherited genetic disposition that makes them

more vulnerable to drinking water contaminants,

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 69

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
people who are exceptionally sensitive to an array of chemical

contaminants, people with specific medical conditions that make them

more susceptible, people with poor nutrition, and people experiencing

socioeconomic hardships and racial or ethnic discrimination.

• Transparency should be incorporated into the design and development of

the classification and decision-making process for future CCLs in addition

to being an integral component in communicating the details of the process

to the public. Otherwise, the public may perceive the process as subject to

manipulation to achieve or support desired results. Therefore, sufficient

information should be provided such that citizens can place themselves in a

position similar to decision-makers and arrive at their own reasonable and

informed judgments. This may require making available to the public the

software and databases used in the process.

• The central tenet that the public is, in principle, capable of making wise and

prudent decisions should be recognized and reflected in the choice of

public participation procedures used to help create future CCLs. A “decideannounce-defend” strategy that involves the public only after the

deliberation process is over is not acceptable. Substantive public

involvement should occur throughout the design and implementation of the

process. EPA should strive to “get the right participation” (i.e., sufficiently

broad participation that includes the range of interested and affected

parties) as well as to “get the participation right” (e.g., incorporating public

values, viewpoints, and preferences into the process).

SOCIOPOLITICAL CONSIDERATIONS FOR DEVELOPING FUTURE CCLS 70

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
3

The Universe of Potential Contaminants to

the Preliminary CCL

INTRODUCTION

As noted in Chapter 1, the Safe Drinking Water Act (SDWA)

Amendments of 1996 require the U.S. Environmental Protection Agency (EPA)

to periodically publish a list (the Drinking Water Contaminant Candidate List

[CCL]) of unregulated contaminants and contaminant groups that are known or

anticipated to occur in public water systems and which may require regulation

(EPA, 1998b). The CCL is intended primarily to provide the basis for a

mandated EPA decision whether to regulate or not at least five new (CCL)

contaminants every five years (see Figure 1–1). More generally, each

successive CCL is intended to be the source of priority contaminants for the

agency’s drinking water program as a whole, including research, monitoring,

and guidance development. The first CCL, published in March 1998, was

created under very pressing time restraints by EPA with significant input from

the National Drinking Water Advisory Council (NDWAC) Working Group on

Occurrence and Contaminant Selection. The limitations associated with its rapid

development were summarized in the committee’s second report (NRC, 1999b)

and are described more fully in Chapter 2 of this report. To a large extent, it was

the internal and external recognition of these limitations and the corresponding

call for a more systematic, scientifically defensible, and transparent approach to

the creation of future CCLs that led EPA to request the formation of this

National Research Council (NRC) committee.

TWO-STEP APPROACH

In its second report (NRC, 1999b), the committee proposed a conceptual

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 71

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
two-step process (see Figure 1–3) for the creation of future CCLs that would

take a much broader approach to contaminant selection than that used to create

the 1998 CCL and, thus, would have a better potential to identify high-risk

contaminants. At EPA’s request, the committee evaluated, expanded, and

revised as necessary the conceptual approach and related conclusions and

recommendations from its second report to form the majority of this report.

Therefore, it is important to note that although the basic concept for the CCL

development approach has not changed, many of the associated guidelines and

recommendations for its design and implementation have necessarily been

revised and expanded in accordance with this second phase of committee

deliberations. The committee continues to recognize, however, that the need for

policy judgments by EPA cannot and should not be removed from any CCL

development process. Furthermore, in making decisions regarding the

placement of a contaminant on a preliminary CCL (PCCL) or CCL, EPA should

use common sense as a guide and err on the side of public health protection.

This chapter provides some initial guidance and recommendations for

conducting the first step of the CCL development process. Consistent with the

inclusive nature of the recommended process, the first step is to consider a

broad universe of chemical, microbial, and other types of potential drinking

water contaminants and contaminant groups. The total number of contaminants

in this universe is likely to be on the order of tens of thousands of substances

and microorganisms, given that the Toxic Substances Control Act inventory of

commercial chemicals alone includes about 72,000 substances (NRC, 1999b). It

is from this universe that contaminants will be selected first for inclusion on a

PCCL—which may include a few thousand contaminants—and then on a

corresponding CCL. Thus, the creation of a PCCL from the universe would

entail an almost two-order-of-magnitude reduction of potential drinking water

contaminants. The inherent difficulties associated with such a task are discussed

in Chapter 2.

As noted earlier, the recommendation that EPA begin by identifying and

assessing the universe of potential drinking water contaminants to arrive at a

PCCL represents a dramatically larger set of substances to be considered

initially in terms of types and numbers of contaminants than that used for

creation of the 1998 CCL. Because of the proposed size of this universe, wellconceived screening criteria must be developed that can be rapidly and

routinely applied in conjunction with expert judgment to cull the universe of

contaminants to a much smaller PCCL. Thus, the PCCL may be thought of as a

much more manageable and less conceptual

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 72

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
list than the universe of potential contaminants. As such, a PCCL should

contain individual substances and groups of related substances, including

microorganisms, that merit further consideration for inclusion on the CCL.

However, the committee continues to recommend that the preparation of a

PCCL should not involve extensive analysis of data, nor should it directly drive

EPA’s research or monitoring activities (NRC, 1999b).

THE UNIVERSE OF POTENTIAL DRINKING WATER

CONTAMINANTS

Recognizing that no single comprehensive list of potential drinking water

contaminants exists, the committee previously identified nine major categories

(with twelve subcategories) of individual and related groups of substances and

microorganisms that comprise the universe of potential drinking water

contaminants (NRC, 1999b).1 These are listed in Table 3–1, along with

examples of each category or subcategory. It is important to recognize that

many contaminants (including the examples provided) could belong to more

than one of these categories.

The committee continues to recommend that EPA begin identifying the

universe of potential contaminants by considering the categories and

subcategories of potentialcontaminants listed in Table 3–1. Furthermore, EPA

should start this task by relying on databases and lists that are currently

available (see Tables 3–2 and 3–3 for chemicals and Table 3–4 for

microorganisms) and under development along with readily available

information to supplement them. However, while relevant databases and lists

exist for many of these categories of potential drinking water contaminants,

many have no lists or databases (e.g., products of environmental degradation).

Thus, EPA should develop a strategy for filling the gaps and updating the

existing databases and lists of contaminants (e.g., through involvement of the

NDWAC or panels of experts) for future CCLs. This strategy should be

developed with public, stakeholder, and scientific community input. In addition,

to generally assist in identifying

1According to SDWA Section 1401(6), “The term ‘contaminant’ refers to any

physical, chemical, biological, or radiological substance in water.” This definition has

not been revised since the inception of the SDWA in 1974 and, thus, includes nontoxic

and potentially beneficial “contaminants.”

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 73

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 3–1 Universe of Potential Drinking Water Contaminants

Category Examplesa

Naturally occurring

substances

Nitrates, humic acid, terpines, arsenic, lead, radon

Microbial agents

Naturally occurring agents in

water

Legionella, toxic algae

Agents associated with human

feces

Enteric viruses, coxsackie B viruses, rotavirus

Agents associated with human

and animal feces

Enteric protozoa and bacteria, Cryptosporidium,

Salmonella

Agents associated with human

and animal urine

Nanobacteria, microsporidia

Agents associated with water

treatment and distribution

systems

Biofilms, Mycobacterium

Chemical agents

Commercial chemicals Gasoline and additives, chlorinated solvents,

trichloroethylene, 1,4-dioxane, cumene

Pesticides Atrazine, malathion

Pharmaceuticals Diclofenac (anti-inflammatory), acetaminophen

(analgesic), ethynllestradiol (estrogen)

Cosmetics Stearates, glycols

Food additives Butylated hydroxyanisole, propylene glycol, dyes

Water additives, including

impurities

Aluminum

Water treatment and

distribution system leachates

and degradates

Vinyl chloride, chloroform

Products of environmental

transformation of chemical

agents

Dichlorodiphenyldichloroethylene,

trichloroacetic acid

Reaction and combustion byproductsAnthracene, benzopyrene, 2,3,7,8-

tetrachlorodibenzodioxin,

Metabolites in the

environment

Methylmercury, dimethylarsenic, dibutyltin,

Radionuclides Radon, iodine-131, strontium-90

Biological toxins Endotoxin, aflatoxin

Fibers Asbestos

a

 Some examples can belong to more than one category of contaminants (e.g., enteric viruses might

also be associated with animal feces).

SOURCE: Adapted from NRC, 1999b.

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 74

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 3–2 Examples of Existing and Planned Information Sources for Chemicals

Demonstrated or Having the Potential to Cause Adverse Health Effectsa

Name Responsible Agency or

Organization

Notes

Endocrine Disruptor

Priority-Setting

Database

EPA Health effects data on

endocrine disruption

collected from a variety

of databases

Everything added to

Food in the United

States Database

U.S. Food and Drug

Administration (FDA)

Toxicologic information

on 2,000 substances

added to food

Hazardous Substances

Data Base

National Library of

Medicine (NLM)

Summary of peerreviewed health effect

studies (about 2,000)

Integrated Risk

Information System

EPA Official EPA summary of

health effects information

and reference doses or

concentrations for

approximately 600

compounds

International Agency

for Research on Cancer

(IARC)

IARC Expert group summaries

of carcinogenic

properties for a wide

variety of substances and

mixtures

MEDLINE NLM Abstracts of peerreviewed studies in

medical literature

National Research

Council

NRC Expert group publications

summarizing health

effects information,

critical end points, and

doses (e.g., arsenic, radon)

Peer-reviewed

published literature

Individual studies about

health effects and related

information (e.g.,

metabolism)

Registry of Toxic

Effects of Chemical

Substances

National Institute for

Occupational Safety and

Health

Tabulation of effect

levels for many

substances reported in

scientific literature

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 75

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Name Responsible Agency or

Organization

Notes

Toxic Substances

Control Act Test

Submissions—Health

Effects

EPA Information on

unpublished health

effects data for industrial

chemicals

TOXLINE NLM Abstracts of peerreviewed toxicologyrelated studies

a

 Includes acute and chronic health effects, such as genotoxicity, developmental toxicity,

reproductive toxicity, immunotoxicity, and carcinogenicity.

SOURCE: Adapted from EDSTAC, 1999; NRC, 1999b.

the universe of potential contaminants and a PCCL, the committee

recommends that EPA consider adding substances based on their commercial

use, environmental location, or chemical characteristics as listed in Table 3–5.

As an integral part of the developing future CCLs, the committee continues

to recommend (NRC, 1999b) that the information used from any such lists be

combined in a consolidated database to provide a consistent mechanism for

recording and retrieving information on the contaminants under consideration.

The database should be designed to accommodate a wide variety of chemicals,

microorganisms, mixtures of agents, and other types of potential contaminants

that are not necessarily defined by a unique chemical formula.

Thus, the database could function as a “master list” that contains a detailed

record of how the universe of potential contaminants was identified and how a

particular PCCL and its corresponding CCL were subsequently selected. The

design, creation, and implementation of such a database should be made in open

cooperation with the public, stakeholders, and the scientific community, the

importance of which is discussed in Chapter 2.

Notably, this recommendation is similar to a recommendation from EPA’s

Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC)

for the development of a database for setting priorities for screening and testing

various substances for endocrine disruption (EDSTAC, 1999). After its report

was published, EPA began development of the Endocrine Disruptor PrioritySetting Database (EDPSD) in response to that recommendation (ERG-EPA,

2000). This database, now

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 76

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 3–3 Examples of Existing and Planned Information Sources for Identifying

Chemicals with Demonstrated or Potential Occurrence in Drinking Water Supplies

Name Responsible Agency or

Organization

Notes

Chemicals in Consumer

and Cosmetic Products

FDA Information on

chemicals that have been

registered voluntarily by

manufacturers

Comprehensive

Environmental Response,

Compensation, and

Liability Act Information

System

EPA Contaminant data for

Superfund sites

Endocrine Disruptor

Priority-Setting Database

EPA Health effects on

endocrine disruption

collected from a variety

of databases.

Environmental Monitoring

and Assessment Program

EPA Monitoring information

for air, groundwater,

surface water, biota, and

soil contaminants

Food Quality Protection

Act (FQPA) “Cumulative

to Pesticides” List

FDA List of chemicals

satisfying FQPA

statutory requirements of

being cumulative to

pesticides

Generally Regarded as

Safe Substances

FDA Ingredients that can be

added to food

National Drinking Water

Contaminant Occurrence

Database

EPA First release online in

August 1999; superceded

by current second

release, which became

operational in August

2000; see Chapter 1 for

further information

National Human Exposure

Assessment Survey

EPA Surveys designed to

assess human exposure

via multiple pathways

(food, water, air, dust)

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 77

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Name Responsible Agency or

Organization

Notes

National Stream Quality

Accounting Network

U.S. Geological Survey

(USGS)

Water quality data for

large subbasins of rivers

National Water-Quality

Assessment Program

USGS Contaminant monitoring

data for surface and

ground water, some data

available on-line

Permit Compliance

System

EPA Information on municipal

and industrial wastewater

discharge

Priority-Based

Assessment of Food

Additives (PAFA)

Database

EPA Contains administrative,

chemical, and

toxicological information

on more than 2,000

substances added directly

to food

Toxic Substances

Control Act Inventory

and Updates

EPA Production volumes and

sites for industrial

chemicals

Toxics Release Inventory EPA Information about a select

number of chemicals

Unregulated

Contaminant

Monitoring Rule

(UCMR)

EPA First UCMR List

published September 17,

1999; see Chapter 1 for

further information

SOURCE: Adapted from EDSTAC, 1999; NRC, 1999b.

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 78

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 3–4 Examples of Existing and Planned Information Sources for Occurrence

and Health Effects of Water-Associated Microbial Agents

Name Responsible Agency or

Organization

Notes

FoodNet Centers for Disease

Control and Prevention

(CDC)

Provides data on incidence

of diseases associated with

key enteric bacteria

GenBank National Institutes of

Health-National Library

of Medicine

Internet-based database

with information on gene

sequences for key

microorganisms (see

Chapter 6 for further

information)

Land use data and

mapping (e.g., sewage

discharge, number of

farms or heads of

livestock)

USGS, states

National Ambulatory

Medical Care Survey

National Center for

Health Statistics (NCHS)

of the CDC

Conducted in 1990, the

survey provided data from

office-based physicians

through examination of

patient records and gave an

indication of the number of

persons who seek a

physician and are diagnosed

National Animal

Health Reporting

System

U.S. Department of

Agriculture Animal and

Plant Health Inspection

Service, Veterinary

Service, and Centers for

Epidemiology and

Animal Health

Pilot project begun in

March 1998, it will include

all 50 states reporting on

disease cases in

commercial livestock

(cattle, sheep, swine,

poultry, fish)

National Hospital

Discharge Survey

NCHS Begun in 1988, the survey

assesses the number of

patients treated in hospitals

National Mortality

Followback Survey

NCHS Represents about 1% of

U.S. resident deaths

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 79

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Name Responsible Agency or

Organization

Notes

National Notifiable

Diseases Surveillance

System

NCHS Compiles U.S. statistics

on diseases. Reported

cases are summarized by

type of disease, reported

month, state, age, and

race in some cases. The

data represent only

clinically identified

cases and case ratios

(cases to total

population) or incidence

rates that are most often

reported annually

National Waterborne

Disease Outbreak

Database

CDC, EPA Catalogs reporting

waterborne disease

outbreaks since 1920

State department of

health data

By state Generally, state health

departments report cases

of disease by county

SOURCE: Adapted from NRC, 1999b.

operational and undergoing advanced development and refinement, could

serve as a starting point for EPA in the selection of chemicals for the PCCL

(and CCL), or EPA could create a new database that might be used for multiple

purposes. Indeed, in a demonstration of its recommended process for going

from a PCCL to a CCL, the committee relied extensively on the EDPSD to help

score several occurrence attributes for a wide variety of chemicals (see Chapters

4 and 5).

In its present form, the EDPSD could be used to help provide EPA with

information about chemical occurrence or potential occurrence in drinking

water since most readily available data sources that would be useful for

occurrence screening are already included in the database. However, obtaining

information about health effects is somewhat more problematic. The EDSTAC

database understandably focuses on obtaining information on health end points

that are related to endocrine disruption rather than on the complete spectrum of

health effects that drinking water contaminants may elicit. While expanding the

EDPSD to include all types of potential drinking water contaminants and their

health effects would require considerable effort and expenditure of resources,

this may

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 80

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 3–5 Additional Considerations for Identification of the Universe of Potential

Drinking Water Contaminants

Potential to Occur in Drinking Water Potential to Cause Adverse Health Effects

Any gasoline additive, constituent, or

contaminants of a petroleum product

Any substance “routinely” stored in an

underground storage tank

Any halogenated hydrocarbon

Any constituent found in a landfill

leachate

Any soluble component of “normal”

soil (e.g., arsenic)

Any disinfectant by-product

Any constituent of wastewater

treatment or septage

Any chemical produced in “high

volume” (use a cutoff)

Any compound found in sludge leachate

Any compound widely applied to land

Any pharmaceutical excreted in urine

or feces

Any chemical routinely reported in a

major biomonitoring program or study

Any military munitions

Any substance purposely intended to

“affect” living systems

Any carcinogen, mutagen, or teratogen

Any compound on the Centers for

Disease Control and Prevention’s “Biosafety List”

Any substance identified in an

epidemiological study that is associated

with an elevated measure of health risk

Any hormonally active compounds

Any enzyme inhibitor or inducer

Any behavioral modifier

Anything that perturbs gene function

Anything “flagged” by structure-activity

relationship, quantitative structureactivity relationship or virulence-factor

activity relationshipa

 analysis

a

 The feasibility of developing and applying virulence-factor activity relationships, or VFARs, for

use in identifying potential waterborne pathogens for regulatory consideration is the subject of

Chapter 6 of this report.

prove less costly and more timely than creating an entirely new PCCLCCL comprehensive database.

DISTINGUISHING THE PCCL FROM THE UNIVERSE

In the committee’s second report (NRC 1999b), contaminants from the

universe are included on the PCCL if they (1) are known to have adverse health

effects and the potential to occur in drinking water, (2) are known to occur in

drinking water (unless known not to pose a significant health risk), or (3) are

believed to pose potential drinking water risks as identified through occurrence

criteria (e.g., release data, production data,

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 81

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
fecal loading) and health criteria (e.g., toxicity data, structure-activity

relationships [SARs], clinical data). However, additional conceptualization and

refinement of the PCCL have occurred since it was first introduced as a major

step in the process of generating future CCLs.

The committee now recommends the Venn diagram in Figure 3–1 as a

useful way to view the PCCL as a subset of the universe of potential drinking

water contaminants. The four shaded, intersection areas labeled I-IV represent

the confluence of two major characteristics of a contaminant necessary for

indicating whether it may cause a public health risk through exposure via

drinking water.

These two characteristics are a contaminant’s demonstrated or potential

occurrence in drinking water and its intrinsic ability to produce adverse health

effects in exposed persons. It is important to note that to

FIGURE 3–1 Conceptual approach to identifying contaminants for inclusion

on a PCCL through the intersection of their demonstrated and potential

occurrence in drinking water and ability to cause adverse health effects. Note,

the sizes of the intersections and rings in this Venn diagram are not drawn to

scale and do not represent an estimate of the relative numbers of contaminants

in each area.

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 82

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
effectively delineate the four “rings” for health effects and occurrence (and their

intersections) from the universe of potential drinking water contaminants

requires the development and use of screening criteria that define

“demonstrated” versus “potential” for each characteristic (see more below).

Otherwise, the use of a Venn diagram concept for the PCCL is meaningless.

However, this conceptualization recognizes the importance of contaminants for

which health effects or occurrence have not been demonstrated (but are

possible), thus allowing for inclusion of emerging contaminants with limited

data.

The contaminants that fall in intersection I are the highest priorities for

placement on a PCCL because they have demonstrated occurrence in drinking

water and are demonstrated to cause adverse health effects. Contaminants that

fall in intersections II and III are of equivalent importance and have medium

priorities for inclusion on a PCCL since they have either demonstrated

occurrence in drinking water or demonstrated adverse health effects in

conjunction with a potential to cause adverse health effects or a potential to

occur in drinking water, respectively. Lastly, contaminants that have the

potential to occur in drinking water and the potential to cause adverse health

effects fall into intersection IV and have the lowest priority for inclusion on a

PCCL. The committee expects that the drinking water contaminants of

intersection I will comprise the smallest assemblage of contaminants, while

intersection IV will comprise the largest group. For examples of Venn diagram

contaminants see Table 3–6.

A significant remaining challenge lies in defining criteria that are to be

used by EPA in conjunction with expert judgment to cull the universe of

potential drinking water contaminants to a few thousand on the PCCL.

Unfortunately, the committee was not able to deliberate extensively on the

development of such criteria because sufficient time was not available to do so.

The committee notes that this outcome was not unexpected, as reflected in the

committee’s statement of task for this second phase of work (see preface to this

report). Thus, the PCCL is still treated on a largely conceptual basis as it was in

the committee’s second report (NRC, 1999b). The committee cautions that the

Venn diagram in Figure 3–1 should not be interpreted to imply that a

contaminant with limited or no data could not be included on a PCCL.

Theoretically, a contaminant that is in the universe of potential drinking water

contaminants and lying outside of all the intersections or even the rings in the

diagram could be placed on a PCCL solely through the use of expert judgment.

In this regard, the committee again notes that in formulating a PCCL, EPA should

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 83

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 3–6 Examples of Venn Diagram Contaminants

Area of Venn Diagram Chemical Microbial

Area I Aldicarb

Diethylhexyl phthalate

Lead

Vinyl chloride

Campylobacter jejuni

Cryptosporidium parvum

Escherichia coli O157:H7

Area II n-Hexane

Salicylate

Cyclospora

Helicobacter pylori

Microsporidia

Area III Dacthal degradates

(DCPA)

Pseudomonas aeruginosa

Area IV Pyrethrin

Triethylene glycol

Cyanobacteria

Nanobacteria

Demonstrated

occurrence ring

Citric acid

Phosphate

Potassium ion

Coliphage

Potential occurrence ring Acetic acid

Glucose

Glycine

Stachybotrys

Demonstrated health

effects ring

Sodium pentothal Ebola virus

Haemophilus influenzae

Streptococcus pneumoniae

West Nile virus

Potential health effects

ring

Bilirubin Cyanophage (viruses for

blue-green algae)

In universe but not in

any ring

Inert gases (argon,

helium)

Methanobacter spp.

NOTE: DCPA=(Dacthal) dimethyl-2,3,5,6-tetrachlorobenzene-1,4-dicarboxylate.

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 84

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
use common sense as a guide and err on the side of public health protection.

GUIDELINES FOR DEVELOPING SCREENING CRITERIA

Criteria for Potential and Demonstrated Health Effects

The information that can be used to identify contaminants with potential

and demonstrated health effects includes human data from epidemiological

studies, clinical studies, and case reports; toxicological laboratory animal

studies or field studies; and predictive biological activity or effects models (e.g.,

SARs). The feasibility of developing and using virulence-factor activity

relationships, or VFARs, to identify emerging waterborne pathogens is

discussed in Chapter 6. The term toxicological laboratory animal studies

includes many types of studies such as whole studies, metabolic studies, and so

forth. The committee recommends that human data and data on whole animals

be used as indicators of demonstrated health effects and that other toxicological

data and data from experimental models that predict biological activity be used

as indicators of potential health effects. The committee has already provided

general guidelines to evaluate and assess health effects data and prioritize the

importance of their findings for deciding how to address contaminants already

on a CCL (NRC, 1999a). However, these principles continue to apply and may

be used to assist in evaluating potential drinking water contaminants for

inclusion on a PCCL.

Potency is an important characteristic of a substance’s ability to cause

health effects. Of course, every substance can have some health effect, given a

sufficiently high dose. However, the dose at which it causes adverse health

effects, especially compared to the concentrations that may occur in drinking

water, is an important consideration when selecting a substance for the PCCL.

As the exposure to a substance from drinking water nears the dose that may

cause health effects, it becomes more important to consider including that

substance on the PCCL. Furthermore, substances can have a variety of health

effects that range from minor and reversible to irreversible, and life threatening.

In general, greater consideration should be given to including substances on the

PCCL that cause serious, irreversible effects as opposed to those that cause less

serious effects. The committee is not suggesting that less serious effects such as

cholinesterase inhibition should be ignored; however, it recognizes that health

effects such as cancer or birth defects may be

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 85

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
given greater weight. The issues of a contaminant’s potency and the severity of

its health effects are discussed in greater detail in Chapter 4 for going from a

PCCL to a CCL.

Criteria for Potential and Demonstrated Occurrence

A variety of metrics could be used to define occurrence of contaminants in

drinking water. These are identified in a hierarchical framework in the

committee’s first report (NRC, 1999a) and include (1) observations in tap

water, (2) observations in distribution systems, (3) observations in finished

water of water treatment plants, (4) observations in source water, (5)

observations in watersheds and aquifers, (6) historical contaminant release data,

and (7) chemical production data. The committee recommends that the first four

of these should be used as indicators of demonstrated occurrence and

information from items 5 through 7 should be used to determine potential

occurrence.

Recorded observations of contaminants in tap water, distribution water,

finished water, or source water can be defined in terms of concentration,

frequency of occurrence over time, and prevalence with respect to the number

of facilities showing detects or people exposed. The committee believes that,

generally, contaminant concentration alone should not be used as a relevant

metric for culling from the universe of contaminants to those that will appear on

the PCCL, although the committee recognizes that some consideration of

concentration may be necessary as analytical procedures continue to reduce

detection limits. EPA may want to consider binary data, such as found or not

found in public water systems, for selecting chemicals for the PCCL from the

universe. Also, the committee believes that frequency over time should not be

used as the sole relevant metric for this step because it may place undue

emphasis on contaminants that are found repeatedly and eliminate those that

may have a significant impact but occur infrequently. The committee believes

that prevalence at a large number of public water systems or prevalence at

systems that serve large numbers of people is an important metric to determine

inclusion into the demonstrated occurrence category. The appropriate threshold,

in terms of number of facilities or number of people exposed, will have to be

decided by EPA, which will also have to consider the manageability of the size

of the resulting PCCL.

Of the metrics that serve as indicators of potential occurrence, the

committee recommends that EPA use production or release data, combined with

physical properties, to serve as useful indicators of the potential

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 86

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
for chemical occurrence in watersheds and aquifers. These properties include

persistence and mobility in aquatic systems. For chemical contaminants, these

may include aqueous solubility, octanol-water partition coefficient, Henry’s law

constant, and recalcitrance. To screen initially for inclusion on the PCCL, which

is appropriate at this stage, aqueous solubility could be used as the sole metric.

For microbial contaminants, viability in ambient waters and particle charge or

hydrophobicity are appropriate. The use of such properties reduces the reliance

on recorded observations in watersheds or aquifers, data that are typically

sparse. If necessary and in the absence of these data, contaminant properties

could potentially be estimated using SARs and QSARs (quantitative structureactivity relationships) for chemicals or using VFARS in the future (see

Chapter 6) for microorganisms.

For chemicals, a binary approach would serve to categorize the universe of

chemicals being produced commercially (i.e., it would not include by-products

or chemicals formed in the environment) into four bins for potential occurrence

as illustrated in Figure 3–2. The committee recommends that if such an

approach were used for commercial chemicals, all chemicals except those with

those with both low production volume and low water solubility should be

considered for inclusion on a PCCL. Furthermore, as indicated by the number

of “Xs,” such a categorization could also be used to roughly prioritize

commercial chemicals for PCCL consideration. An example of a chemical in

the high-water-solubility and high-production or release bin is MTBE (methyl-tbutyl ether). The aromatic hydrocarbon cumene is a good example of a

chemical in the low-water-solubility and high-production or release bin. An

example of a chemical in the high- water-solubility and low-production or

release bin is the perchlorate ion. Finally, the steroid anti-inflammatory drug

dexamethasone is an example of a low-production, low-water-solubility

chemical that would not be expected to occur in high concentration in

FIGURE 3–2 Categories for relative potential of chemicals to occur in

drinking water based upon consideration of production volume and water

solubility.

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 87

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
drinking water. (However, expert judgment in considering the potent biological

effects of this compound might well place it on a PCCL.) In this case, the

challenge for EPA is to determine scientifically defensible and transparent

thresholds for defining “high” and “low” for the two metrics. Again, these

thresholds should be chosen considering the manageability of the size of the

resulting PCCL.

It is important to note that several potential categories of drinking water

contaminants would be missing after this initial screening exercise (see Tables

3–1 and 3–5). For example, EPA should review contaminants already included

in the potential occurrence category (“ring”) to determine if they have any

important environmental degradation products, production or reaction byproducts, or metabolites in the environment that should also be considered for

inclusion on the potential occurrence list. EPA should also review naturally

occurring substances and fibers to determine whether any of them should be

included on the potential occurrence list. EPA may also want to review data for

specific watersheds and aquifers to determine if any other contaminants should

be included on the potential occurrence list. The committee again notes that its

recommendation for the development and use of a well-designed,

comprehensive database would greatly support the task of developing future

PCCLs.

FATE OF THE PCCL

As noted earlier, the committee recommends that an entirely new PCCL be

created every five years as a precursor to the development of the corresponding

CCL. This should encourage a more thorough and timely consideration of the

contaminants that comprise the PCCL than may occur if new substances were

simply added to the previous PCCL (excluding those reaching the CCL). Thus,

current and past PCCLs would be retained and used in conjunction with (or in

lieu of and acting as) a comprehensive relational database useful for recording

and understanding the process used to select the PCCL and ultimately the CCL.

The committee notes that it is entirely reasonable to expect that prior PCCLs

will be reviewed and thereby utilized as an obvious starting point for the

creation of subsequent PCCLs. However, a former constituent of a PCCL may

not appear on a new PCCL for a variety of reasons, such as the availability of

new data that indicate it does not occur in drinking water or does not cause

adverse health effects. In keeping with its inclusive

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 88

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
nature, the PCCL should not be expected to maintain a more or less fixed

number of potential drinking water contaminants.

SUMMARY: CONCLUSIONS AND RECOMMENDATIONS

The first CCL, published in March 1998, was prepared in a short time

period by EPA with the assistance of NDWAC to meet the statutory

requirements and mandated time line of the SDWA Amendments of 1996. The

limitations associated with its necessarily rapid development have been

summarized in the committee’s second report (NRC, 1999b) and are fully

described in Chapter 2 of this report. While the contaminants included on the

first CCL certainly merit regulatory and research consideration, a broader

approach to contaminant selection could potentially identify higher-risk

contaminants. As previously noted, the NRC Committee on Drinking Water

Contaminants continues to recommend that EPA develop and use a two-step

process for the creation of future CCLs along with several related

recommendations (listed below), many of which were originally described in

the committee’s second report (NRC, 1999b). However, several other

associated guidelines and recommendations for CCL design and

implementation have been revised and expanded as a result of the committee’s

second phase of deliberations and are also listed below. The committee

continues to recognize that the need for policy judgments by EPA cannot and

should not be removed from any CCL (or PCCL) development process. In

making these decisions, EPA should use common sense as a guide and err on

the side of public health protection.

Regarding the first step of this process (going from the universe of

potential drinking water contaminants to a PCCL), the committee makes the

following recommendations:

• EPA should begin by considering a broad universe of chemical, microbial,

and other types of potential drinking water contaminants and contaminant

groups (see Table 3–1). The total number of contaminants in this universe

is likely to be on the order of tens of thousands of substances and

microorganisms, given that the Toxic Substances Control Act inventory of

commercial chemicals alone includes about 72,000 substances (NRC,

1999b). This represents a dramatically larger set of substances to be

considered initially in terms of types and numbers of contaminants than

that used for the creation of the 1998 CCL.

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 89

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
• EPA should rely on databases and lists that are currently available (see

Tables 3–2 and 3–3 for chemicals and Table 3–4 for microorganisms) and

under development, along with other readily available information to begin

identifying the universe of potential contaminants that may be candidates

for inclusion on the PCCL. For example, EPA should consider using the

EDPSD to help develop future PCCLs (and perhaps CCLs).

• While relevant databases and lists exist for many “universe categories” of

potential drinking water contaminants, others have no lists or databases

(e.g., products of environmental degradation). Thus, EPA should initiate

work on a strategy for filling the gaps and updating the existing databases

and lists of contaminants (e.g., through involvement of the NDWAC or

panels of experts) for future CCLs. This strategy should be developed with

public, stakeholder, and scientific community input.

• As an integral part of the development process for future PCCLs and CCLs,

all information from existing or created databases or lists used should be

compiled in a consolidated database that would provide a consistent

mechanism for recording and retrieving information on the contaminants

under consideration. Such a database could function as a “master list” that

contains a detailed record of how the universe of potential contaminants

was identified and how a particular PCCL and its corresponding CCL were

subsequently created. It would also serve as a powerful analytical tool for

the development of future PCCLs and CCLs. As a starting point, the

committee recommends that EPA review its developing EDPSD database

to determine if it can be expanded and used as this consolidated database or

can serve as a model for the subsequent development of such a database.

Regardless, the (re)design, creation, and implementation of such a database

should be made in open cooperation with the public, stakeholders, and the

scientific community.

• To assist generally in the identification of the universe of potential

contaminants and a PCCL, the committee recommends EPA consider

substances based on their commercial use, environmental location, or

physical characteristics as listed in Table 3–5.

• EPA should be as inclusive as possible in narrowing the universe of

potential drinking water contaminants down to a PCCL. The committee

envisions that a PCCL would contain on the order of a few thousand

individual substances and groups of related substances, including

microorganisms, for evaluation and prioritization to form a CCL. However,

preparation of a PCCL should not involve extensive analysis of data, nor

should it directly drive EPA’s research or monitoring activities.

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 90

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
• The committee recommends the use of a Venn diagram approach (see

Figure 3–1) to conceptually distinguish a PCCL from the universe of

potential drinking water contaminants. However, because of the extremely

large size of the universe of potential drinking water contaminants, wellconceived screening criteria remain to be developed that can be rapidly and

routinely applied by EPA in conjunction with expert judgment to cull the

universe to a much smaller PCCL. Thus, the PCCL should include those

contaminants that have a combination of characteristics indicating that they

are likely to pose a public health risk through their occurrence in drinking

water. These characteristics are demonstrated or potential occurrence in

drinking water and demonstrated or potential ability to cause adverse health

effects.

• Regarding the development of screening criteria for health effects, the

committee recommends that human data and data on whole animals be

used as indicators of demonstrated health effects and that other

toxicological data and data from experimental models that predict

biological activity be used as indicators of potential health effects.

• A variety of metrics could be used to develop screening criteria for the

occurrence of contaminants in drinking water. These are identified in a

hierarchical framework in the committee’s first report (NRC, 1999a) and

include (1) observations in tap water, (2) observations in distribution

systems, (3) observations in finished water of water treatment plants, (4)

observations in source water, (5) observations in watersheds and aquifers,

(6) historical contaminant release data, and (7) chemical production data.

The committee recommends that the first four of these should be used as

indicators of demonstrated occurrence and information from items 5

through 7 should be used to determine potential occurrence.

• For commercial chemicals, their potential to occur in drinking water may

be estimated using a combination of production volume information and

water solubility (see Figure 3–2). Most likely occurrence would involve

high-production-volume chemicals with high-water-solubility.

• A new PCCL should be generated for each CCL development cycle to

account for new data and emerging contaminants.

• Each PCCL should be published and thereby serve as a useful record of

past PCCL and CCL development and as a starting point for the

development of future PCCLs.

• Development of the first PCCL should begin as soon as possible to support

the development of the next (2003) CCL; each PCCL should

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 91

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
be available for public and other stakeholder input (especially through the

Internet) and should undergo scientific review.

THE UNIVERSE OF POTENTIAL CONTAMINANTS TO THE PRELIMINARY CCL 92

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
4

PCCL to CCL: Attributes of Contaminants

INTRODUCTION

As described earlier in this report, the committee continues to recommend

a two-step process for the creation of future Drinking Water Contaminant

Candidate Lists (CCLs) (see Figure 1–3). This chapter provides some guidance

and recommendations for conducting the second step of the CCL development

process: selecting preliminary CCL (PCCL) contaminants for inclusion on the

corresponding CCL through use of a prioritization tool in conjunction with

expert judgment. In general, the preliminary CCL (PCCL) may be thought of as

a much more manageable and less conceptual list than the universe of potential

contaminants. As such, the PCCL is anticipated to contain up to a few thousand

individual substances and groups of related substances, including

microorganisms, that merit further consideration for inclusion on the CCL.

However, it is anticipated that nearly all of the contaminants on a PCCL will

have incomplete information on their potential occurrence and health effects.

Thus, any process for selecting PCCL contaminants for inclusion on a CCL

must recognize and overcome such limitations. Furthermore, the absence of

information for a particular PCCL contaminant should not necessarily be an

obstacle to its inclusion on the CCL, as recommended in the committee’s

second report (NRC, 1999b). In all cases, some amount of expert judgment will

be required for the assessment and promotion of each PCCL contaminant to its

corresponding CCL.

OVERVIEW OF CONTAMINANT ATTRIBUTES

The committee’s recommended approach to this daunting problem

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 93

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
was to develop and use five attributes that contribute to the likelihood that a

particular PCCL contaminant or group of related contaminants could occur in

drinking water at levels and frequencies that pose a public health risk. In this

regard, the committee devised a scoring system for each of these five attributes

whereby the highest-priority PCCL contaminants are selected in conjunction

with expert judgment for inclusion on a CCL. The five attributes are divided

into health effect and occurrence categories. For health effects, the committee

identified severity and potency as key predictive attributes. Prevalence,

magnitude, and persistence-mobility comprise the occurrence attributes.

It is important to note that the committee spent a great deal of time

deliberating on the number and type of contaminant attributes that should be

used in the prototype classification algorithm approach recommended for use

(in conjunction with expert judgment) in the development of future CCLs.

Ultimately, the committee decided that these five contaminant attributes

constitute a reasonable starting point for the U.S. Environmental Protection

Agency (EPA) to consider, especially since they were subsequently found to

aptly demonstrate the utility of the recommended CCL development approach

(see Chapter 5). Furthermore, the metrics and related considerations presented

in this chapter for scoring each attribute should similarly be viewed as

illustrative. Thus, the committee does not explicitly or implicitly recommend

these five (or necessarily a total of five) attributes or the related scoring metrics

as being ideally suited for direct adoption and use by EPA. Rather, should EPA

choose to adopt a classification approach for the development of future CCLs,

the committee recommends that options for developing and scoring

contaminant attributes should be made available for public and other

stakeholder input and undergo scientific review.

Severity

The question of severity may be stated simply as, How bad is the health

effect? In terms of this report, severity can be scored using the most sensitive

health end point for a particular contaminant (e.g., the health effect that occurs

at the lowest dose compared to other health effects reportedly caused by the

contaminant) and considering vulnerable subpopulations (see Chapter 2). In

other words, for the most sensitive health end point, what is the anticipated

clinical magnitude in affected individuals? The committee recommends that the

assessment of severity

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 94

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
should be based, when feasible, on plausible exposures via drinking water.

This information can be ascertained from clinical reports, from animal

bioassay results, or by inference from effects of similar compounds.

Information from epidemiological studies, structure-activity relationships, or

future virulence-factors activity relationships (see Chapter 6) may also prove

useful. For illustrative purposes, a 10-level hierarchy for scoring the severity of

health effects may be defined as in Table 4–1. It should be noted that in

developing this table, it was necessary to consider the chronicity of the health

effect.

An alternative approach to evaluating severity would be to perform a

ranking using either quality adjusted or disability adjusted life-years (QALYs or

DALYs), lost respectively, due to exposure to a contaminant. In this regard,

various weighting scales can be used, each of which to some degree

incorporates economic and social considerations of disease impact (Bowie et

al., 1997; Havelaar et al., 2000; Hyder et al., 1998; Mauskopf and French,

1991). However, as noted in Chapter 2, the committee cautions that the use of

approaches such as QALYs and DALYs may not adequately protect vulnerable

subpopulations (Arneson and Nord, 1999). Thus, the committee recommends

that EPA give consideration to different severity metrics.

Potency

Potency may be expressed simply as, How much of a contaminant does it

take to cause illness? This is a relative scaling of the dose-response relationship.

For carcinogens, an obvious metric for scaling is the cancer slope factor (q1),

which is defined as the incremental risk divided by incremental dose in the lowdose region (EPA, 1999h). Databases of these potencies are available, such as

EPA’s Integrated Risk Information System (see Table 3–2; EPA, 2000f). A

method for scaling carcinogenic contaminants would be to use the percentile of

the contaminant’s potency relative to the potencies of all contaminants being

considered, including those drinking water contaminants with enforceable

maximum contaminant levels (MCLs). The percentile (0–100) scale could then

easily be converted to a 1 through 10 (decile) scale. For mutagens, a similar

percentile ranking can be derived using bioassays results such as the Ames test.

For noncarcinogenic contaminants, a logical basis of comparison is the

benchmark dose (BMD10), which can be defined simply as the dose

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 95

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 4–1 Scoring of Severity Attribute

Severity Score Characteristic

0 No effect

1 Changes in organ weights with minimal clinical significance

2 Biochemical changes with minimal clinical significance

3 Pathology of minimum clinical importance (e.g., fluorosis, warts,

common cold)

4 Cellular changes that could lead to disease; minimum functional

change

5 Significant functional changes that are reversible (e.g., diarrhea)

6 Irreversible changes; treatable disease

7 Single organ system pathology and function loss

8 Multiple organ system pathology and function loss

9 Disease likely leading to death

10 Death

corresponding to a 10 percent excess risk above background levels

(Crump, 1995). These can be computed for continuous (Crump, 1995), quantal

(Gaylor et al., 1998), and to a lesser extent, graded responses (Gibson et al.,

1997). BMDs for a contaminant can then be scaled by percentile BMD for that

contaminant relative to the BMDs of all contaminants being considered,

including contaminants with MCLs; the percentile scale can similarly be

converted to a 1 through 10 scale. Alternatively, the lowest observed adverse

effect level (LOAEL) or the no observed adverse effect level (NOAEL) dose1

can be used.

For infectious agents, a logical basis of comparison is the median

infectious dose (N50), defined as the dose from a single exposure leading to

infection of 50 percent of a population (Haas et al., 1999). This may be defined

with reference to current MCLs.2

1 The lowest dose that results in a statistically or biologically significant increase in

the frequency or severity of adverse health effects between an exposed group and an

appropriate control group and the lowest dose that does not, respectively. 2 At the time of this writing, there are no MCLs for individual microbial pathogens—

only treatment requirements. Thus, it may be appropriate to define the percentile with

reference to pathogens for which treatment techniques have been set. At present, this

would include Giardia lamblia and human enterovirus (both of which are regulated

under the Surface Water Treatment Rule), Legionella pneumophila (which is regulated

by implication under the Surface Water Treatment Rule), and Cryptosporidium parvum

(which is to be regulated under the Enhanced Surface Water Treatment Rule).

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 96

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
If contaminants act via diverse health effect end points (e.g., a carcinogen

that also has noncarcinogenic effects), then an appropriate potency score would

be the worst (i.e., the highest) score of all of the relevant effects in order to err

on the side of public health protection.

When no experimental data are available to infer the potency of a chemical

contaminant, the use of a (quantitative) structure-activity relationship should be

considered. In the case of microbial agents, quantitative virulence-factor

activity relationships (see Chapter 6) may become available in the future.

Prevalence

The attribute of prevalence may be defined as, How commonly does or

would a contaminant occur in drinking water? Ideally, prevalence should be

assessed based first on measurements in tap water, followed by measurements

in distribution systems, finished water of water treatment plants, and source

waters used for drinking water supply as discussed in the committee’s first

report (NRC, 1999a) and earlier in this report (see Chapter 3). If such data are

not available, inferences may have to be made from general watershed or

aquifer measurements, historical contaminant release data, or even chemical

production data. The focus of this attribute is the geographical and temporal

range (i.e., sporadic or episodic versus frequent) of occurrence or anticipated

occurrence. Thus, prevalence has both spatial and temporal aspects.

For illustrative purposes, spatial and temporal prevalence can be

consolidated into a single prevalence index using an approach such as that

summarized in Table 4–2. Temporal prevalence represents the average fraction

of time that a contaminant is found at a given locale (if multiple locations are

sampled, the percentage of occurrence times at each site should be averaged

among sites). Spatial (or geographical) prevalence represents the proportion of

locales in which the contaminant would be found, such as all communities with

public water systems for which contaminant data are available. For the purpose

of assessing geographical prevalence, sites in the same watershed should be

regarded as the same site. For example, if the temporal prevalence averaged 66

percent and the spatial prevalence averaged 80 percent, a score of 9 would be

the result.

There are two important questions to be resolved with respect to assessing

prevalence. First, in the absence of data either on temporal prevalence (e.g.,

many sites each examined only at a single point) or on

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 97

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 4–2 Overall Prevalence Score Related to Temporal and Spatial Prevalence

Temporal Prevalence (%)

Spatial Prevalence (%) <25 25–50 50–75 75–90 90–100

<25 1 2 3 4 6

25–50 2 4 5 7 8

50–75 3 5 8 9 9

75–90 4 7 9 10 10

90–100 6 8 9 10 10

spatial prevalence (e.g., one site examined many times), what score should

be assigned? Second, prevalence may have to be defined with respect not to an

absolute detection value (e.g., above the detection limit) but to a prevalence

above some level likely to be of concern. If the latter is not done, the percentage

prevalence on either a temporal or a spatial basis is anticipated to increase as

analytical methods advance, since samples that would previously have scored as

“nondetects” would be scored as “detects.”

With respect to the first issue, if only temporal or only spatial prevalence

information is available, then it may be appropriate to determine and use a

default assumption in which the spatial prevalence and the temporal prevalence

(in the absence of information on both factors) are assumed to be equal. In the

second case, the committee believes that further consideration regarding the

issue of decreasing detection limits is in order.

Furthermore, the committee notes that in many cases (particularly where

contaminants have been included on a PCCL on the basis of potential rather

than demonstrated occurrence), insufficient information will be available to

assess temporal or spatial prevalence (or both) directly. In such circumstances,

this attribute must then go unscored. However, the absence of prevalence

information, as described below, will not necessarily preclude PCCL

contaminants from being included on the corresponding CCL.

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 98

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Magnitude

Magnitude can be defined as the concentration or expected concentration

(e.g., based on chemical production) of a contaminant relative to a level that

causes a perceived health effect. In lay terms, this might be stated, Is the level

high enough to cause harm? However, the issue is not simply the absolute

magnitude of a contaminant, but rather magnitude relative to potency.

As in the case of prevalence, if a substance has been included on a PCCL

on the basis of potential rather than demonstrated occurrence, then data would

not generally be available to evaluate (even on a 10-point scale) the magnitude

of expected concentration in drinking water.

With substances for which at least some concentration measurements exist,

a suitable metric for evaluating the magnitude would relate the median (as

opposed to the mean) water concentrations to the potency. To score this

attribute, the median water concentration and a measure of potency have to be

combined in a simple and consistent manner to yield a numerical measure of

scale. As noted previously, the committee recognizes that the available

information with which to go from a PCCL to a CCL will almost always be

imperfect and will often be poor. Thus, the following approach to scoring

magnitude is suggested for illustrative purposes.

First, the median water concentration for the contaminant under evaluation

is ranked relative to the numerical values for median occurrence of

contaminants with MCLs,3 on a decile basis (1–10 ranking, with 10 being

compounds present at highest magnitude relative to the MCL medians4). This

percentile is next multiplied by the converted decile score for potency and the

square root is taken (e.g., if a compound is in the 9th decile for occurrence and

its potency is given a score of 3, then

3 Because of obvious differences in measurement approaches, chemical contaminants

and microbial contaminants should be ranked separately with respect to chemical and

microbial MCL values. Due to the current lack of specific MCLs for microorganisms

(except coliform organisms and heterotrophic plate count organisms), it may be

appropriate to develop “apparent MCLs” for each of the microorganisms regulated under

a treatment technique option (i.e., Giardia lamblia, human viruses, Legionella

pneumophila, Cryptosporidium parvum). 4 Under the authority of the Safe Drinking Water Act Amendments of 1996, EPA has

recently assembled expansive data on the concentrations of regulated compounds in

public water supplies (EPA, 1999a).

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 99

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
the magnitude score is ). It is important to note that this

calculation gives greater weight to contaminants with many large-magnitude

occurrences and high relative potencies than to contaminants that have fewer

but higher-magnitude occurrences but are of low relative potency. In addition,

contaminants that have many low-magnitude occurrences but high relative

potencies will also receive less weight.

Persistence-Mobility

The persistence-mobility attribute of a contaminant is intended to describe

the likelihood that the contaminant would be found in the aquatic environment

based solely on its physical properties. For PCCL contaminants that have

demonstrated occurrence in water, the occurrence attributes of prevalence and

magnitude should be scored and take precedence over their persistence-mobility

scores. However, in the absence of data on occurrence, persistence and mobility

should be used to assess the potential for significant occurrence of PCCL

contaminants in drinking water.

There are many chemical fate and persistence models that could be

adapted for the current purpose. Recent examples include Bennett et al. (2000)

and Gouin et al. (2000); however, most models require data on too many

contaminant properties to be practical for screening perhaps thousands of

chemicals on the PCCL. In addition, there appears to have been little prior

effort to develop persistence-mobility metrics that are suitable for ranking

chemicals and microorganisms together. To overcome these limitations, the

committee recommends consideration of three general characteristics of

contaminants that would foster their persistence and/or mobility in water

systems:

1. high potential for amplification by growth under ambient conditions

(applies to microbial contaminants and not to chemicals);

2. high solubility in water (applies primarily to chemicals), although the

transportability of microorganisms may be assessed through

sedimentation velocities and size and adsorption capabilities; and

3. stability in water (i.e., resistance to degradation via mechanisms such as

hydrolysis, photolysis, or biodegradation in the case of chemicals; death

or the ability to produce nonculturable or resistant states [e.g., spores

and cysts] in the case of microorganisms).

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 100

About this P

DF file: This new digital representation of the original work has been recomposed from X

ML files created from the original paper book, not from the

original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be

retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
It should be possible to assess these characteristics for a large number of

contaminants (both chemical and microbial) by applying a simple,

semiquantitative scoring scheme such as that illustrated in Tables 4–3 to 4–5. In

this system, a score of 1 represents a characteristic that is relatively unfavorable

persistence-mobility in water (low amplification, solubility, or persistence),

while a score of 3 represents a characteristic that is relatively favorable

persistence in water (high amplification, solubility, or persistence). More

specifically, any given chemical or group of related chemicals would be scored

using Tables 4–4 and 4–5, whereas microorganisms would be scored primarily

using Tables 4–3 and 4–5. If the individual characteristics are scored according

to this scheme, an overall score for persistence-mobility can be obtained on a

scale of 1 to 10 by taking the arithmetic average of the scores and multiplying

by 10/3.

TABLE 4–3 Scoring Contaminants for Microbial Amplification

Subscore Doubling Time Under Environmental Conditions

Low (1) >1 week

Medium (2) 1 day-1 week

High (3) <1 day

TABLE 4–4 Scoring Contaminants for Solubility

Subscore Solubility (mg/L)

Low (1) 0.1

Medium (2) 0.1–10

High (3) >10

TABLE 4–5 Scoring Contaminants for Stability

Subscore Half-Life (Combined, All Mechanisms)

Low (1) <1 day

Medium (2) Days to weeks

High (3) >Weeks

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 101

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
LESSONS LEARNED IN APPLYING THESE CRITERIA

The contaminant attributes and associated scoring criteria outlined in this

chapter were applied to a panel of chemical and microbial validation test

contaminants. The details of this demonstration are given in Chapter 5 to

illustrate the utility of using such a classification approach for the creation of

future CCLs. During the course of this demonstration, a number of procedural

issues were discovered with respect to implementation of the approach. The

following material summarizes the major factors identified during the scoring of

various health effect and occurrence attributes.

Monitoring Data

Monitoring data are important in the scoring scheme, for both the

prevalence and the magnitude attributes. For convenience in conducting this

exercise, the committee relied extensively on EPA’s developing Endocrine

Disruptor Priority-Setting Database (EDPSD) to obtain usable monitoring data

for chemicals (ERG-EPA, 2000). The committee commends EPA for beginning

to obtain and compile environmental monitoring data as exemplified by the

creation and ongoing development of the EDPSD in addition to the Unregulated

Contaminant Monitoring Regulation List and the National Drinking Water

Contaminant Occurrence Database (NCOD) described in Chapter 1. It is

important to note, however, that monitoring data in the EDPSD from water

supplies are limited, both in the number of chemicals for which monitoring data

exist and in the amount of sampling. Without additional monitoring data

compiled in a comprehensive database that facilitates priority setting (as

recommended in Chapter 3), EPA will be hampered in terms of the quality of its

priority-setting exercise and the ease of completing that exercise.

As noted earlier, some of the most relevant monitoring data for prioritysetting activities in going from a PCCL to a CCL are data from water supplies.

In keeping with this assertion, the committee relied, whenever possible, on

drinking water monitoring data from the recently established NCOD as

maintained in EDPSD. Earlier in this chapter, the committee described scoring

criteria that use information on temporal and geographic occurrence. Because

such data are not available in this particular database, however, the committee

instead used information on

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 102

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
population exposed and number of analyses with detects for prevalence.

Expanding on the recommendation in Chapter 3 that EPA review the EDPSD

database to determine whether it can be used to help develop a PCCL and

perhaps help select PCCL contaminants for inclusion on a CCL, the committee

also recommends that EPA consider the possibility of including information on

temporal and regional occurrence. However, the committee notes that the

EDPSD does contain exposure information from the U.S. Geological Survey’s

National Stream Quality Accounting Network and National Water-Quality

Assessment Program, both of which include regional water quality information.

Additionally, although information on the number of analyses with detects is

included in the EDPSD and NCOD, information on the total number of analyses

is not. Without denominator data, the usefulness of information on the number

of detects is limited.

The magnitude score uses information on potency and the average

concentration. Although some information on median concentration is included

in the occurrence data in EDPSD, the median concentration is calculated using

only the detected concentrations; nondetected values are not included. The

median thus does not provide a true picture of the median concentration of a

chemical in drinking water if nondetected values are not included and especially

if the majority of observations are nondetects. EPA should develop a method of

expressing an “average concentration” value that uses as much of the data,

including nondetects, as possible. The literature suggests several approaches to

this issue (Gilliom and Helsel, 1986; Haas and Scheff, 1990; Helsel and Cohn,

1988). In addition, EPA may want to consider providing other measures of

concentration in water supplies, such as the 95th percentile of contaminant

concentration. The committee can envision other scoring schemes that might

use monitoring data rather than relying on median values. For example, a

chemical may occur in a relatively limited geographic area of the United States

at high concentrations and not be found to a great extent in other areas of the

country; under this circumstance, the chemical may still warrant inclusion on

the CCL.

Potency Compared to Monitoring Data

The potency score for chemicals was calculated using LOAELs,

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 103

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
NOAELs, and RfDs5 from readily available health effects-related databases

such as EPA’s Integrated Risk Information System (IRIS) (EPA, 2000f).

Although the number of chemicals for which these data exist is limited, the

quality of the data for scoring this attribute was often high. This contrasts to

information contained in the monitoring database that was used extensively

(EDPSD). The difference in the quality of these databases is not unexpected,

however, given that IRIS has existed for more than a decade, while monitoring

databases are in much earlier stages of development.

Severity

In this demonstration, severity was scored for chemicals based on the

health effect associated with the LOAEL. In some cases, this could result in a

fairly low severity score because the health effect associated with the LOAEL

was relatively minor such as a decrease in body weight gain. It is important to

note, however, that such a chemical could cause health effects associated with

significantly higher severity scores (e.g., birth defects) at only a slightly higher

dose.

Severity and Potency

For chemicals that caused both cancer and noncancer health effects,

potency was scored for both types of effects and the higher one was used in the

model. The severity score was then based on the corresponding health end point

used (e.g., LOAEL or cancer). In a few cases, the potency score was higher for

the noncancer end point, and the health end point associated with the LOAEL

had a low severity score. Thus, potency was scored first and severity was based

on the end point on which potency was based. Alternatively, severity could

have been scored first and then potency based on the end point associated with

the severity score. Another approach could have scored potency and severity

independently of each other. If the scoring had been done differently, the

modeling results are likely to have been different. This decision to score

5An oral reference dose (or RfD) is an estimate of the concentration of a substance that

is unlikely to cause appreciable risk of adverse health effects over a lifetime of exposure,

including sensitive subgroups (Barnes and Dourson, 1988).

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 104

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
potency and then severity also affected the magnitude score since the potency

score is used in calculating the magnitude score.

Vulnerable subpopulations were considered, when readily feasible, in the

scoring. For example, the health end point ultimately used for scoring the

potency of nitrate (see Box 5–1) was methemoglobinemia in infants (i.e., an

excess of methemoglobin—the oxidized form of hemoglobin—resulting in

cyanosis). In this case, the severity and potency scores were based on the effect

in this population, rather than for adults, and the committee considered this

reasonable. Consistent with the recommendation in Chapter 2 that EPA

consider expanding the current working definition of vulnerable subpopulations

in the amended Safe Drinking Water Act, the committee deems it appropriate

for EPA to consider using health end point data based on vulnerable

subpopulations, especially those that comprise a “meaningful portion of the

general population.”

SUMMARY: CONCLUSIONS AND RECOMMENDATIONS

The committee recommends that EPA develop and use a set of attributes to

evaluate the likelihood that any particular PCCL contaminant or group of

related contaminants could occur in drinking water at levels and frequencies

that pose a public health risk. More specifically, these contaminant attributes

should be used in a prototype classification algorithm approach, such as that

described in Chapter 5, and in conjunction with expert judgment to help identify

the highest-priority PCCL contaminants for inclusion on a CCL. In this chapter,

the committee has presented a scoring system and related considerations for a

total of five health effect and occurrence attributes. For health effects, the

committee identified severity and potency as key predictive attributes;

prevalence, magnitude, and persistence-mobility comprise the occurrence

attributes.

Although the committee spent a great deal of time deliberating on the

number and type of contaminant attributes that should be used in the

recommended CCL development approach, ultimately, it decided that these five

attributes constitute a reasonable starting point for EPA to consider.

Furthermore, the scoring metrics and related considerations for each attribute

should be viewed as illustrative. Thus, the committee does not explicitly or

implicitly recommend that these five (or that there should be five) attributes the

related scoring metrics be adopted directly for use by EPA. If EPA chooses to

adopt a prototype classification approach for the development of future CCLs,

the committee recommends that options for developing and scoring

contaminant attributes should be made available

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 105

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
for public and other stakeholder input and undergo scientific review. The

committee also makes the following related recommendations:

• The assessment of severity should be based, when feasible, on plausible

exposures via drinking water. The committee also recommends that EPA

give consideration to different severity metrics such as a ranking through

use of either quality adjusted or disability adjusted life-years lost due to

exposure to a contaminant.

• Regarding the assessment of contaminant prevalence, in some cases

(particularly where contaminants have been included on a PCCL on the

basis of potential rather than demonstrated occurrence), information will

often be insufficient to directly assess temporal or spatial prevalence (or

both). Thus, EPA should consider the possibility of including information

on temporal and regional occurrence to help determine (score PCCL)

contaminant prevalence. When prevalence cannot be assessed, this attribute

must then go unscored and the attribute of persistence-mobility used in its

stead. The issue of changing (or incorporating) “thresholds” for

contaminant detection, rather than relying on continually decreasing

detection limits, is one that needs explicit attention and discussion by EPA

and stakeholders.

• Existing and readily available databases may not be sufficient to rapidly

and consistently score health effect and occurrence attributes for individual

PCCL contaminants for promotion to a CCL. As recommended in

Chapter 3, all information from existing or created databases or lists used in

the development of a CCL and PCCL, should be compiled in a

consolidated database that would provide a consistent mechanism for

recording and retrieving information on the PCCL contaminants under

consideration. As a starting point and as recommended in Chapter 3, EPA

should review its developing EDPSD database to determine if it can be

expanded and used (or serve as a model for the development of) such a

consolidated database and to help develop future PCCLs and CCLs.

• Contaminant databases used in support of the development of future CCLs

should report summary statistics on all data collected, not only the

quantifiable observations. In this regard, EPA should formalize a process

for reporting means and/or medians from data with large numbers of

nondetect observations. In addition, EPA may want to consider providing

other measures of concentration in water supplies such as the 95th

percentile of contaminant concentration.

PCCL TO CCL: ATTRIBUTES OF CONTAMINANTS 106

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
5

PCCL to CCL: Classification Algorithm

INTRODUCTION

The intrinsic difficulty of identifying potentially harmful agents for

resource-intensive scrutiny such as the selection of a drinking water

contaminant from a preliminary Drinking Water Contaminant Candidate List

(PCCL) for inclusion onto a CCL raises the question of what kind of process or

method is best suited to this judgment. As discussed in Chapter 2, the sorting of

perhaps thousands of PCCL contaminants into two discrete sets, one (the CCL)

that probably will undergo research or monitoring of some sort preparatory to

an eventual regulatory decision and another much larger set that will not, is an

exercise in classification.

The committee considered three broad types of strategies for

accomplishing this task: expert judgment, rule-based systems, and prototype

classifiers. It is important to note at the outset that there are no sharp boundaries

between these three classification strategies since all are based on data to some

extent and expert judgments factor into all to some degree. The main

differences are the specific mix of data and expert judgment and the extent to

which explicit or implicit judgments come together to produce predictable

results. This chapter begins with a general overview of classification schemes.

It is followed by an example application, framework, and related

recommendations for how the U.S. Environmental Protection Agency (EPA)

might develop its own prototype classification scheme for use in the creation of

future CCLs.

PCCL TO CCL: CLASSIFICATION ALGORITHM 107

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
OVERVIEW OF CLASSIFICATION SCHEMES

Expert Judgments

Many decisions (and their associated classifications) are made on the basis

of the collective experience of experts. EPA has made extensive and regular use

of expert committees (including this National Research Council [NRC]

committee), advisory panels, peer review committees, and the like to help its

staff (another assemblage of experts) make important policy decisions.

Clearly the composition of any expert group is critical to the outcome, and

in recent years there has been a concerted effort within EPA to include a wide

spectrum of “stakeholder” opinion in the expertise solicited (e.g., the September

1999 workshop to discuss EPA’s draft CCL Research Plan; AWWARF, 2000).

This is a recognition that where a particular expert stands on the issue is often

influenced by where he or she sits in the real world (e.g., organizational

affiliations). Of course, the outcome of any expert panel may be influenced

equally, though to a largely unknown extent, by the absence of persons who

could not participate in such meetings. Often the reasons for the absence of a

particular perspective are random or accidental, as when schedules or timing do

not permit participation at the last minute or result in a substitution for similar

reasons.

Yet even when matters external to the question at hand are set aside, the

dynamics of expert committees often influences the outcome in crucial ways.

Thus, when a particular subject comes up (e.g., at the beginning of a meeting

versus the end of a long day), who advocates for or against a position (involving

questions of articulateness, seniority, and status) or the juxtaposition or context

of agenda items can lead to very different outcomes for reasons unconnected

with the content of the issues at hand. The presence or absence of EPA experts

and the relative force-fulness of their participation can frequently alter the

direction of a discussion in important ways, for example, directing it toward or

away from regulatory and policy concerns that may not be apparent or

uppermost in the minds of non-agency experts. For the same reasons,

discussions of committees comprised solely of EPA staff and consultants are

likely to have a different character than those with significant or predominant

participation from experts outside the agency.

One strategy to neutralize these adventitious effects is to use a formal

PCCL TO CCL: CLASSIFICATION ALGORITHM 108

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Delphi procedure. The Delphi technique was introduced more than 30 years ago

to limit interaction among participants and thereby optimize the quality of

decisions (Dalkey, 1969; Linstone and Turoff, 1975). It is one of the formal

group judgment methods used to obtain consensus and define disagreements on

a specific question (Webler et al., 1991). The method assists in identifying

important issues and needs (NRC, 1996, 1999c), integrating data and judgments

(NRC, 1988, 1992), informing policy processes (NRC, 1996), structuring

deliberations, modifying classification schemes, and ranking priorities (IOM,

1988; NRC, 1992). It may be used to obtain consensus on a time-limited issue

or to facilitate longer-term building of common ground on issues. For example,

the technique has been used by committees of the Institute of Medicine (IOM,

1992) and is being used at the U.S. Army’s Tooele Chemical Agent Disposal

Facility to identify current issues to be included in a stakeholder survey (NRC,

1999c).

Briefly, the Delphi process includes the solicitation and compilation of

responses to a specific question. The answers are summarized (typically

displayed with the range and mean of all responses) and provided to the

participants, with each person invited to reconsider his or her position in light of

the compilation. The answers received from this second request are again

compiled. This process is repeated until group consensus is obtained (IOM,

1995; NRC, 1988). The technique is traditionally conducted with participants

responding remotely and independently of each other. The Delphi method has

been implemented with mailed or faxed surveys, e-mail instruments, face-toface meetings or workshops, and iterations of closed computer networks and

group dialogues. The means selected to implement the Delphi process involve

trade-offs of cost, time, and commitment of the participants; the impact of

influential individuals; confidentiality; duration and intensity of the process;

response rate; and clarity of the responses (IOM, 1995).

The Delphi technique may be implemented alone or as part of a multistep

process such as Renn and colleague’s three-step procedure discussed in

Chapter 2 (Renn et al., 1993), or it may be conducted for separate, dissimilar

groups, commonly referred to as a double-Delphi system (NRC, 1992). For

example, the same question could be asked of experts and nonexperts convened

in two separate but simultaneous panels. The double-Delphi approach may be

employed in workshops where the groups use separate computer networks in

separate rooms and then share their consensus in reporting-out sessions with all

groups present.

Although there has been relatively little evaluation of the effectiveness

PCCL TO CCL: CLASSIFICATION ALGORITHM 109

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
of the Delphi process, there are indications that it may not result in decisions

much improved over those obtained through less structured processes (IOM,

1992). Its use for selecting candidates for the CCL, therefore, is questionable,

but it remains a technique that potentially avoids some of the traditional pitfalls

of expert opinion methods of classification.

Rule-Based Methods

Rule-based schemes take as inputs various features or parameters of an

object and weigh and combine these features according to an algorithm that is

decided upon in advance—usually as a result of some expert judgment. One

characteristic of rule-based and expert systems is that their classification

strategy is what is often called “Aristotelian,” that is, objects (in this case

potential drinking water contaminants) are assigned attributes (e.g., toxicity or

solubility), and a set of rules is used to determine which class they are in

(Bowker and Star, 1999). They are, thus, expert opinion strategies, in which the

opinions are embedded into a fairly rigid algorithm. Their rigidity can be

considered both their strength (they are objective and consistent, and they allow

for high throughput) and their weakness (they do not easily allow for additional

nuanced judgment). The weights and modes of combination used in a rulebased scheme are presumably determined using some preexisting idea or

objective of what the scheme intends to capture; however, this is often not done

in any systematic or explicit fashion.

The committee’s first report reviewed 10 rule-based ranking schemes

whose objective was to use data about chemicals to establish a priority for

regulation or attention (NRC, 1999a). As noted in that report, there are no

formal schemes that might be considered for prioritizing microbial

contaminants. However, EPA and the American Water Works Association

Research Foundation have jointly sponsored a series of expert workshops

beginning in 1996 to develop a decision process for prioritizing emerging

waterborne pathogens. Although the final report of these workshops is nearing

completion, it was not available for review at the time this report was written.

At the request of EPA, the rule-based systems were evaluated for their potential

to help select contaminants (including microorganisms) that are already on a

CCL for future action. All of the systems reviewed had at least one

shortcoming, but of special concern was the extent to which (often) arbitrary

and nonexplicit expert

PCCL TO CCL: CLASSIFICATION ALGORITHM 110

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
judgments were intrinsically embodied in what appeared to be objective ranking

schemes. For example, various kinds of information (such as chemical

persistence, solubility, and toxicity) had to be weighted and combined to

produce a single number that allowed an ordinal ranking at the end of the

process. The methods of combination (e.g., additive or multiplicative) and the

weights given the various factors were all matters of judgment that were in

place prior to the input of any data to the ranking system.

In short, the committee concluded that a ranking process that attempts to

sort contaminants in a specific order is not appropriate for the selection of

drinking water contaminants already on a CCL for regulation, research, or

monitoring activities (NRC, 1999a). In the absence of complete information, the

output of the prioritization schemes was found to be so uncertain (though this

uncertainty is generally not stated) that they are of limited use in making more

than preliminary risk management decisions about drinking water contaminants.

The committee concluded, however, that they may provide a (semi)quantitative

means for preliminarily screening and sorting large numbers of contaminants.

Even with this limited use, however, the aforementioned considerations can still

become serious issues.

Based on its collective experience, the committee emphasizes that there are

some common and often troublesome problems that occur in developing new

ranking systems and revising existing ranking systems to suit specific purposes.

Many of these problems are similar to those noted earlier as related to the

formation and deliberations of expert panels. For example, sometimes an expert

group contains individuals who interpret the charge or understand the client’s

intended purpose differently, because they bring different disciplines,

experiences, and/or motives to the process. A perhaps surprisingly common

problem is that many such groups think they have a consensus about the

purpose of the system on which they are working, but in fact do not. Even when

a goal is clearly stated and mutually understood, group members may envision

different types of ranking processes and final products as appropriately meeting

the goal. Some group members may believe a simple ranking process is

adequate, whereas others believe the process should be somewhat to very

complex. The final product may be conceived as quantitative, semiquantitative,

or qualitative, with a whole range of outputs from a few to many and from

categorical to specific. Some groups have had long debates about what process

and form will best fit the purpose, and some have come to near deadlock over

how much scoring or ranking is

PCCL TO CCL: CLASSIFICATION ALGORITHM 111

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
enough or too much. Others have worked for months before they realized that

they were trying to achieve different visions.

Another situation in which groups that are trying to develop ranking

schemes have encountered difficulties is where science and policy issues meet.

When there is a lack of clarity about which decisions are within the group’s

charge and which are better left to policy settings, groups have often gotten

focused on narrow scientific issues, either not recognizing that there is a policy

issue in the matter or inadvertently believing that there is a scientific “solution”

to a policy problem. Groups have also gotten bogged down in specific scientific

issues when it has been too difficult to address broader points of disagreement.

A report sponsored by the Society of Environmental Toxicology and Chemistry

is available that might help reduce such problems because it provides an expert

panel consensus framework along with principles and guidelines to promote

consistency in the development and use of chemical ranking and scoring

systems (Swanson and Socha, 1997).

Prototype Classification Methods

An alternative classification strategy is sometimes referred to as “prototype

classification” (Bowker and Star, 1999). This strategy recognizes that in

ordinary practice and discourse one does not usually classify objects on the

basis of a fixed algorithm, but instead uses criteria based on prior classification

of examples or prototypes. The classic example is character recognition of an

individual letter in handwriting, most commonly recognized by its similarity to

an idealized example rather than by any fixed features such as height-to-width

ratios. Prototype classifiers take advantage of the prototyping activity at which

humans generally (and intuitively) excel.

Prototype classification schemes usually take the form of neural networks,

clustering algorithms, machine learning classifiers, and their hybrids. These

methods start with a known classification of prototypes (a “training set”) that

embody the kinds of outcomes one might wish to achieve. These prototypes are

used to discern an algorithm that maps prototype features or attributes into

classification outcomes. The prototype-based algorithm can then be used for

classification of new objects. The neural network paradigm is discussed more

fully later in this chapter.

PCCL TO CCL: CLASSIFICATION ALGORITHM 112

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Application of a prototype scheme for constructing the CCL would consist

of a training set of chemicals, microorganisms, and other types of (potential)

drinking water contaminants that would clearly belong on the CCL, such as

currently regulated chemicals (if they were not already regulated), and those

that clearly do not, such as food additives generally recognized as safe by the

U.S. Food and Drug Administration. Each contaminant’s “features” or

“attributes” must be extracted and characterized. In this case, these would

include parameters such as solubility, various measures of toxicity (quantitative

or categorical), and occurrence data, if any. Using this as a training set, the

neural network constructs both the mode of combination and the weighting

factors that seem best to differentiate between the two categories.

It is important to note that the distinction between Aristotelian and

prototype classification strategies is not sharp but is more a matter of emphasis.

What in the ranking scheme was done a priori by experts is done instead on the

basis of the data that differentiated prototype examples. This a posteriori

determination of weights on the basis of features sets these methods apart from

rule-based methods, but it should be clear that rule-based methods also depend

on the data in a fundamental way. Conversely, prototype schemes emphasize

past classification decisions in the construction of a classification algorithm but

necessarily involve some expert judgment in determining which features should

be used to characterize the prototypes.

The committee notes that prototype strategies have found use in commerce

and other sciences, although their use for constructing something like the CCL

would be innovative. Keller et al. (1995) have compiled papers describing

studies of neural network applications in hazardous waste disposal,

environmental monitoring, and reliability analysis. The Federal Aviation

Administration has used a neural network for analysis of radiation directed at

travelers’ luggage at security check-points; numerous financial analysts have

found neural networks to be useful in predicting stock performance and foreign

exchange rates; and neural networks have been successful in the prediction of

educational performance. These examples are well described by Garson (1998)

and the references cited therein.

Which Strategy to Use?

To date, EPA has relied extensively on use of expert judgment and to

PCCL TO CCL: CLASSIFICATION ALGORITHM 113

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
a lessor extent on rule-based prioritization schemes to identify and rank

drinking water contaminants for regulatory and research activities. It is clear

that contaminant-by-contaminant consideration by panels of experts as to

whether something should be placed on a CCL is not possible if the entire

universe of potential drinking water contaminants is to be considered, as

recommended in the committee’s second report (NRC, 1999b) and again in this

one. Some efficient screening method is required. Until now, this has usually

meant the use of an a priori ranking or classifying system. However, none of the

existing schemes for ranking chemicals that the committee previously reviewed

(NRC, 1999a) seem able to fulfil this function readily, so one would have to be

significantly modified or an entirely new one created. As discussed above, the

difficulties are formidable, and there is no obvious reason why a new attempt

will solve the problems of previous ones.

Use of a prototype classification strategy and neural network technology

would seem to be an innovative new approach. Furthermore, since the objective

is one of identification, not regulation per se, the method does not have to

withstand regulatory challenge. The PCCL contaminants that it helps identify

for inclusion on a CCL can be evaluated in conventional ways by EPA staff and

external experts for further justification of why they do or do not belong on the

CCL. The recommended system merely identifies and offers likely candidates

in a systematic way.

The committee notes that a potentially serious drawback of using this

technology is its perceived lack of “transparency” in that the neural network can

easily appear as a black box, with little obvious indication of how it is

“working.” The importance of transparency in decision-making is discussed

extensively in Chapter 2. However, the committee reiterates that transparency is

not necessarily synonymous with simplicity. A CCL decision-making process

that uses complex classification modeling can be made relatively transparent by

emphasizing that the classification is based on prototypes of past regulatory

decisions and should, thus, be readily defensible. The difference in this regard

from more conventional methods is perhaps misleading. The committee

emphasizes that there is little that is “transparent” or easily reproducible about

expert judgment, for example. The “black box” in this case is the human brain.

Such expert judgments still must be justified for regulatory purposes, but this is

no different than what will be required as output from a neural network

approach. Ranking systems are only superficially transparent, in that the

weights and modes of combination are explicit and open for all to see,

PCCL TO CCL: CLASSIFICATION ALGORITHM 114

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
but how the weights are arrived at and the consequences of the modes of

combination usually are not. As for the neural network approach, its outputs

must also be justified for regulatory purposes.

The committee recommends, therefore, that EPA give careful

consideration to and actively experiment with developing a prototype

classification approach using neural network or similar methods (in conjunction

with expert judgment) for identifying appropriate PCCL candidates for

inclusion on the CCL. Further, EPA should use several training sets to gauge

the sensitivity of the adopted method. As discussed in Chapter 2, the committee

cautions that disputes may occur (e.g., among decision-makers and between

decision-makers and the public) regarding the issue of what minimum levels of

sensitivity and specificity the approach must have in order to be deemed

“adequate.” A robust result over different training sets would clearly lend

support for this approach. Additional details of the recommended approach to

the development of future CCLs are presented later in this chapter.

ILLUSTRATIVE EXAMPLE OF A PROTOTYPE

CLASSIFICATION SCHEME FOR CCL CONTAMINANTS

The remainder of this chapter presents a demonstration of prototype

schemes for classifying drinking water contaminants. As previously noted,

prototype classification methods require a training data set. For illustrative

purposes, the committee constructed a training data set based on contaminants

that are presumed worthy of regulatory consideration and contaminants for

which the committee can presume regulatory consideration is not necessary.

The contaminants included in the training data set were then assigned values for

the five health effect and occurrence attributes discussed in Chapter 4. For

purposes of mathematical modeling, the report uses the following notation to

represent the values of the five attributes for each contaminant: X1 is severity,

X2 is potency, X3 is prevalence, X4 is magnitude, and X5 is persistence-mobility.

Depending on the category of the contaminant, each contaminant is assigned a

value of the binary classification variable, the target (T). The committee uses

T=1 for contaminants that are “presumed worthy of regulatory consideration”

and T=0 for contaminants “presumed not worthy of regulatory consideration.”

The prototype classification approach does not require that the

contaminant attributes be those specified by the committee. One could use

PCCL TO CCL: CLASSIFICATION ALGORITHM 115

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
more attributes or fewer. They can be different attributes entirely. Redundant

attributes can be integrated prior to processing. For example, in the absence of

complete occurrence information one might use Boolean logic to combine

prevalence and magnitude or persistence-mobility. Multidimensional attributes

could be expanded. For example, the Henry’s law constant and aqueous

solubility for chemicals could be used directly rather than combined in a single

persistence-mobility score. Also, the attributes can be ordinal or categorical.

The objective is to come up with contaminant features that comprehensively

encompass the information that determines a regulatory action decision.

The overall objective is to mathematically represent the mapping between

the contaminant attributes (the values of Xi

s) and the target value. This is

expressed mathematically as

Y=f({Xi};θ), (5–1)

where Y is the predicted value of the classification variable and θ

collectively describes the parameters of the mapping function f(). The most

commonly used metric to evaluate the function’s ability to capture the training

data set is the mean squared error (mse):

(5–2)

where N is the total number of contaminants in the training data set and the

subscript j denotes contaminant j. There is a great deal of choice in selection of

the mapping function f(). In this report, the committee presents two alternatives.

One is based on a linear function, and the other derives from a neural network

architecture. The training data set is used to “calibrate” the mapping function

(i.e., to estimate the optimal values of the parameters).

The final step is to use an appropriate criterion to determine the optimal

threshold value for Y that separates data into the two classes. One possible

criterion is to set the threshold at that value of Y that minimizes the number of

objects in the training data set that are misclassified. The resulting mapping can

then be used to predict the classification for new contaminants to which values

of Xi

 have been assigned. A predicted

PCCL TO CCL: CLASSIFICATION ALGORITHM 116

About this P

DF file: This new digital representation of the original work has been recomposed from X

ML files created from the original paper book, not from the

original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be

retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
value of Y greater than the threshold would indicate that the contaminant

belongs in the T=1 category, and a predicted value of Y less than the threshold

would indicate that the contaminant belongs in the T=0 category.

The following sections of this chapter present the details of such an

analysis. The committee emphasizes that the classification analysis presented

here is not intended to lead to definitive conclusions about specific

contaminants. The committee was limited in time and resources; hence the

training data set used for this analysis was smaller than ideal and the attribute

values are far from certain. Nonetheless, the committee believes that this

analysis provides a valid demonstration of the methodology and could serve as

a framework for a similar analysis that EPA might conduct to help classify

PCCL contaminants for inclusion on the corresponding CCL.

The Training Data Set

The committee constructed a training data set consisting of 80 chemical

and microbial contaminants, 63 of which were assigned to the “presumed

worthy of regulatory action” category (T=1) and 17 of which were assigned to

the “presumed not worthy of regulatory action” category (T=0). The

contaminants included in the training data set are listed in Table 5–1. Those in

the T=1 category were selected from among currently regulated drinking water

contaminants that have enforceable maximum contaminant levels (MCLs; see

Chapter 1 for further information). Those in the T=0 category are considered to

be safe for human ingestion. Some of these were taken from the list of

substances generally recognized as safe (GRAS) by the U.S. Food and Drug

Administration. In discussing this illustrative example, the committee reiterates

that it was constrained by time and data resources, so the list of contaminants in

the T=0 category is relatively small and includes only inorganic and organic

chemicals. If the EPA conducts a similar analysis, the committee strongly

recommends that it increase the size and types of drinking water contaminants

(e.g., radionuclides) included in the training data set to improve predictive

capacity.

PCCL TO CCL: CLASSIFICATION ALGORITHM 117

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
PCCL TO CCL: CLASSIFICATION ALGORITHM 118

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Attribute Scoring

For each contaminant in the training data set and for each of the validation

test cases, values between 1 and 10 were assigned to each of five health effects

and occurrence attributes. The committee used the contaminant attributes and

associated scoring metrics and guidance outlined in Chapter 4. It is important to

note that the contaminant attribute scores for chemicals and microorganisms in

the training data set are considered rough estimates because they were

generated in a very rapid fashion using limited sources of health effects and

occurrence information. Thus, they are not provided in this report. However, the

committee gained a number of important insights by going through this

exercise; these insights form the basis for the “lessons learned” section at the

end of Chapter 4. For the five regulated contaminants that were used as

validation test cases, which are described later in this chapter, details of the

attribute scoring scheme used by the committee are explained in the Box 5–1.

Despite the considerable uncertainty in attribute values in its training data

set, the committee made every effort to be as precise and consistent as possible

within the time frame allowed and feels that this analysis provides some very

valuable insights into the usefulness of the approach. The first component of the

analysis is an examination of the extent to which the attribute values are

correlated (Figure 5–1). For the training data set constructed by the committee,

there is no significant correlation between any of the attributes. This is

interesting because the committee had expected at the outset that there would be

some redundancy between the prevalence scores and the persistence-mobility

scores, reasoning that if something persists in the environment and is mobile in

water bodies, it is more likely to be prevalent in drinking water sources. Further,

the absence of correlation between the attributes is encouraging because it

implies that each contributes unique information that may be useful in the

classification of PCCL contaminants. Figure 5–1 also shows that the values of

each of the attributes are fairly well distributed over the assigned range of 1 to

10.

EPA will have to collect and organize available data and research for each

PCCL contaminant and document the attribute scoring scheme used to help

ensure a transparent and defensible process. One way in which EPA is

encouraged to expand the training data set and classification algorithm is to

allow for the expected case of missing data—that is, to purposefully include in

the training data set contaminants for which values

PCCL TO CCL: CLASSIFICATION ALGORITHM 119

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
BOX 5–1 ATTRIBUTE SCORING FOR VALIDATION TEST

CASES1

Arsenic

Arsenic is an element that occurs naturally in rocks, soil, water, air,

plants, and animals. It is a metalloid that exhibits both metallic and nonmetallic chemical and physical properties and has several valence states.

Although arsenic is found in nature to a small extent in its elemental form

(0 valence), it most often occurs as inorganic and organic compounds in

either the As(III) (+3) or As(V) (+5) valence states (see more below).

There are numerous natural sources (e.g., geologic formations and

volcanic activity) as well as anthropogenic sources (e.g., manufacture of

semiconductors and animal feed additives (EPA, 2000i) of arsenic.

The severity score was assigned based on the health end point

associated with the potency score. For arsenic, the potency score

associated with the lowest observed adverse effect level (LOAEL) for

noncancer effects was higher than the potency score based on its cancer

end point (EPA, 2000c). The adverse health effects at the LOAEL were

hyperpigmentation, keratosis, and possible vascular complications in

humans. Since these effects were considered irreversible changes,

leading to a treatable disease, arsenic was assigned a severity score of 6.

The reference dose (RfD) given in EPA’S Integrated Risk Information

System (IRIS) database for arsenic is 3×10` 4 mg/kg (EPA, 2000c). This is

based on a no observed adverse effect level (NOAEL) of 8×10` 4 mg/kg in

humans. As noted above, the effect at the LOAEL is hyperpigmentation,

keratosis, and possible vascular complications in humans. The range of

LOAELs for the chemicals that were scored varied from a low of 2×10` 8

mg/kg (dioxin) to a high of 1×103 mg/kg (di(2-ethylhexyl) adipate). Based

on the range of LOAEL values and the LOAEL for arsenic, it was assigned

a potency score of 6.

1This box provides several examples of how the attributes of various drinking water

contaminants used in the training data set for this demonstration were individually and

expeditiously scored using publicly and readily available environmental databases as

described in Chapter 4. As such, these scores do not in any way represent an

independent or systematic evaluation of their health effects and occurrence data and

should not be interpreted or cited as such.

PCCL TO CCL: CLASSIFICATION ALGORITHM 120

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
The data used for obtaining arsenic’s prevalence score were taken

from the Endocrine Disrupter Priority-Setting Database (EDPSD) currently

being developed for use by EPA (ERG-EPA, 2000). The database

contains information from the National Drinking Water Contaminant

Occurrence Database (NCOD). Information from the EDPSD-NCOD

about the estimated population exposed (127,350) to arsenic and the

number of analyses with detectable levels (57) of arsenic was used to

assign a prevalence score of 4. The range of population exposed in this

particular database for the chemicals scored was 250 to 327,600, and the

range in analyses with detectable levels was 0 to 2,970.

The EDPSD-NCOD drinking water database also contains

information about contaminant concentration. The EDPSD-NCOD

reported a weighted average concentration in drinking water for arsenic of

410 μg/L. The weighted average concentration in drinking water for the

chemicals scored in this database ranged from a low of 1 μg/L to a high of

8,343 μg/L. A magnitude score of 10 was assigned to arsenic using

information on the weighted average concentration and the potency score.

Scoring the persistence and mobility of arsenic exemplifies some of

the difficulty of this exercise for contaminants exhibiting complex

speciation under environmental conditions. Under oxidizing conditions, the

stable form of arsenic is pentavalent (arsenate), whereas the trivalent

form (arsenite) and various organoarsenic compounds are more prevalent

in reducing environments. While arsenite is more mobile than arsenate,

chemical or biological oxidation-reduction reactions can convert one form

to the other (Nriagu, 1994). Since arsenate adsorbs rather strongly to

soils, the overall mobility of arsenic was scored rather low. However, the

overall persistence of arsenic was scored as high because most

mechanism for detoxifying arsenic appears to be reversible. Thus, the

resulting score for the persistence-mobility attribute was 6.

Nitrate

Inorganic nitrates often enter drinking water supplies through runoff

from fertilizer use, leaching from septic tanks, sewage, and erosion of

natural deposits (EPA, 2000a). The severity score for nitrate was based

on the health effect associated with the potency score, (i.e.,

methemoglobinemia; see Chapter 4 for further information). Since this

health effect is associated with significant, but reversible functional

changes, a severity score of 5 was assigned to nitrate.

PCCL TO CCL: CLASSIFICATION ALGORITHM 121

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
The RfD for nitrate given in EPA’s IRIS database is 1.6 mg/kg (EPA,

2000d). This is based on a NOAEL of 1.6 mg/kg for early clinical signs of

methemoglobinemia in excess of 10 percent in 0- to 3-month-old infants.

A potency score of 4 was assigned for nitrate and was based on its

LOAEL of 1.6 mg/kg and the range of LOAEL values. The scoring for

nitrates was based on an effect in a vulnerable population that contains

many individuals, and the committee considered this reasonable; this is

also consistent with EPA’s current approach for evaluating nitrates.

The data used to estimate nitrate’s prevalence score were taken from

EDPSD-NCOD (ERG-EPA, 2000). The estimated population exposed to

nitrate was given as 118,800 with the number of analyses with detectable

concentrations given as 55. The range of information about population

exposed and number of detects for all of the chemicals considered, along

with this same information for nitrate, resulted in a prevalence score of 3.

The EDPSD-NCOD reported a weighted average concentration for

nitrate in drinking water of 821 μg/L. This average concentration along

with potency information was used to assign a magnitude score for nitrate

of 8.

Many otherwise useful databases (e.g., the Hazardous Substances

Database in TOXNET; NLM, 2000) are inconveniently organized for the

purposes of this exercise since they contain no entry for the free aqueous

nitrate anion—only entries for the various salts of nitrate (KNO3, etc.).

Most of these salts are very soluble, and since sorption of nitrate to most

mineral surfaces is not especially strong, it was considered highly mobile.

The persistence of nitrate varies greatly with the microbiology of the local

environment because nitrate reduction can be quite rapid under anaerobic

conditions but is usually negligible under aerobic conditions. This led the

committee to score nitrate’s persistence as intermediate. Combined with

the highest possible score for mobility, this resulted in a combined

persistence-mobility score of 8 for nitrate.

Atrazine

Atrazine is a herbicide that often enters drinking water supplies

through runoff from its use in agriculture. The severity score was based on

the health effects associated with the potency score for atrazine

(degradation products were not considered) (i.e., a decrease in body

weight gain compared to controls, cardiac toxicity, moderate to severe

dilation of

PCCL TO CCL: CLASSIFICATION ALGORITHM 122

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
the right atrium). Since the cardiac toxicity and moderate to severe

dilation of the right atrium are a single organ system pathology and

function loss, atrazine was assigned a severity score of 7 (EPA, 2000e).

The RfD for atrazine is 3.5×10` 2 mg/kg and is based on a NOAEL of

3.5 mg/kg in rats from a two-year feeding study (EPA, 2000e). The

LOAEL from this study was 25 mg/kg for a decrease in body weight gain.

A one-year feeding study in dogs was used as support for this RfD. The

second study had a NOAEL of 34 mg/kg and a LOAEL of 4.97 mg/kg, with

the adverse effect being cardiac toxicity and moderate to severe dilation

of the right atrium. Atrazine’s potency score was based on its LOAEL of

25 mg/kg. The range of LOAELs for the chemicals that were scored varied

from a low of 2×10` 8 mg/kg (dioxin) to a high of 1×103 mg/kg (di(2-

ethylhexyl) adipate). Based on these LOAEL values, atrazine was

assigned a potency score of 3.

The data used for obtaining atrazine’s prevalence score were taken

from EDPSD-NCOD (ERG/EPA, 2000). Information from EDSPD-NCOD

about the estimated population exposed (223,550) to atrazine and the

number of analyses with detectable levels (24) of atrazine was used to

assign a prevalence score for atrazine of 3. The range for population

exposed in this particular database for the chemicals scored was 250 to

327,600, and the range in analyses with detectable levels was 0 to 2,970.

The EDPSD-NCOD reported a weighted average concentration value

for atrazine of 1 μg/L. This information along with potency data was used

to assign a magnitude score for atrazine of 2.

The persistence and mobility of atrazine and other triazine herbicides

have been studied and reviewed extensively (Ballantine and McFarland,

1998; NLM, 2000). Atrazine is a nonionogenic but moderately polar

organic molecule, so it has a moderate solubility, relatively low volatility,

and moderately high tendency to adsorb to soils. Atrazine is subject to

photodegradation and biodegradation where conditions are favorable.

Reported half-lives for biodegradation of atrazine in soils are typically on

the order of months, whereas half-lives in groundwater are estimated to

be in years. The moderate mobility and substantial persistence of atrazine

resulted in an overall persistence-mobility score of 7.

Tetrachloroethylene (PCE)

Tetrachloroethylene is a solvent that can reach drinking water

supplies through industrial discharges from factories and dry cleaners

(EPA,

PCCL TO CCL: CLASSIFICATION ALGORITHM 123

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
2000a). PCE causes liver tumors in mice when administered by

gavage (ATSDR, 1997). The oral cancer potency factor (CPF) given by

EPA’s Region III for PCE is 5.2×10` 2 mg/kg/day (EPA, 1999i). Because

PCE causes cancer, its severity score was 9.

The potency score for PCE was based on its CPF (EPA, 1999i). The

range in oral CPFs for the chemicals that were scored varied from 1.56× 10 +5 mg/kg/day (dioxin) to 1.2×10` 3 mg/kg/day (di(2-ethylhexyl) adipate).

Based on the CPFs, the potency score for PCE was assigned a value of 3.

The data used for obtaining PCE’s prevalence score were from

EDPSD-NCOD (ERG-EPA, 2000). The estimated population exposed to

PCE was given as 227,600, and the number of analyses with detectable

levels was given as 124. Using this information along with information

from the other chemicals scored resulted in a prevalence score for PCE of

10.

The EDPSD-NCOD gave a weighted average concentration for PCE

of 4 μg/L. This information along with potency information was used to

assign a magnitude score for PCE of 4.

The environmental fate of chlorinated ethenes has been reviewed

from many perspectives (NLM, 2000; Pankow and Cherry, 1996). PCE is

regarded as moderately mobile in natural waters, even though its solubility

in water is fairly low, because retardation due to adsorption is also

relatively low. In general, PCE is regarded as persistent, especially under

aerobic conditions, although both aerobic and anaerobic pathways of PCE

biodegradation are well documented. Combining a moderately high

persistence with moderately mobility resulted in a combined persistencemobility score of 7.

Giardia Lamblia

Giardia lamblia is a protozoan parasite that may reach drinking water

supplies through human and animal fecal waste. It induces mild to severe

diarrhea over a moderately prolonged (one-to-two week) period, with most

cases either self-limiting or amenable to therapy (Steiner et al., 1997).

There do not appear to have been significant reports of chronic sequelae.2

Thus, this organism was assigned a severity score of 6.

2The term “chronic sequelae” is poorly understood and often misapplied as related to the

study of waterborne pathogens. For the purposes of this report, it can be defined as a

secondary adverse health outcome that (1) occurs as a result of a previous infection by a

microbial pathogen and (2) is clearly distinguishable from the health events, if any, that

initially result from the causative infection and (3) lasts three months or more after

recognition (Parkin et al., 2000).

PCCL TO CCL: CLASSIFICATION ALGORITHM 124

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
There are only two pathogens other than G. lamblia for which

regulations are set (or proposed): Cryptosporidium parvum and enteric

viruses. The median infectious dose (N50) of rotavirus is 6.2 plaqueforming units (Regli et al., 1991), while that for C. parvum is 165 oocysts

(Haas et al., 1996). The median infectious dose of G. lamblia is 34 cysts

(Rose et al., 1991). Thus, the potency of Giardia is intermediate between

C. parvum and rotavirus. To obtain a percentile, examination of a broader

spectrum of human pathogens for which dose-response assessments

have been made must be undertaken. The table below, adapted from

Haas et al. (1999), shows that G. lamblia is the 3rd most potent of 11

organisms or classes of organisms. Assigning the most infectious

(adenovirus 4) a score of 10 and the least infectious

(nonenterohemorrhagic Escherichia coli) a score of 1, the potency score

based on rank order would be 8.18, rounded to 8, for G. lamblia.

Based on measurements of G. lamblia in source waters (LeChevallier

et al., 1991, 1997; Rose et al., 1991) a spatial prevalence of 50–75

percent (accounting for both groundwater and surface water) and a

temporal prevalence of 75–90 percent appear to be reasonable estimates.

Thus, a prevalence value of 9 was assigned.

To determine the score for magnitude first involved ranking the

occurrence of Giardia versus other waterborne pathogens. In comparison

with the two other regulated microorganisms (enteric viruses and

Cryptosporidium), the median levels of Giardia in source waters are

higher.3 Thus, it is considered to be in the ninth decile for concentration.

The score, noted above for potency, is 8. The overall score for magnitude

would be rounded to 9.

The two applicable subattributes for the evaluation of persistencemobility of G. lamblia are amplification and stability. Since G. lamblia is an

obligate parasite, there is no reproduction under environmental conditions

and the score for this property is 0 (out of 3). Studies on persistence of the

cyst in natural waters (DeRegnier et al., 1989) indicate a survival time in

excess of weeks; thus, the score on the property of stability is 3 (out of 3).

The average of these two scores, expressed on a scale of 10, is 5, which

is taken as the score for the overall persistence-mobility attribute.

3Based on data collected in response to the Information Collection Rule (i.e., source

waters from communities serving in excess of 100,000 persons).

PCCL TO CCL: CLASSIFICATION ALGORITHM 125

About this P

DF file: This new digital representation of the original work has been recomposed from X

ML files created from the original paper book, not from the

original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be

retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Organism N50

Adenovirus 4 1.66

Rotavirus 6.3

Giardia lamblia 34.8

Cryptosporidium parvum 165

Vibrio cholerae 243

Campylobacter jejuni 896

Salmonella (5 nontyphoid strains)a 23,600

Salmonella typhosa 3.60×106

Escherichia coli (6 nonenterohemorhagic strains)b 8.60×107

a Salmonella newport, derby, bareilly, anatum, and meleagridis. b E. coli O124, O143, O6:H16, O148:H28, O78:H11, and O55.

SOURCE: Adapted from Haas et al., 1999.

of some of the attributes are unknown and develop a scheme that allows

prediction for contaminants for which some of the attributes are unknown.

Statistical schemes have been developed to deal with missing and incomplete

data (e.g., Little and Rubin, 1987; Weiss and Kulikowski, 1990). Some of the

general techniques include filling in the missing values with the means of

values for other cases in the same class, incorporating Boolean logic structures

that allow for redundancy in expression of an attribute (i.e., A or B), and using

smoothing methods to interpolate values based on assumed underlying data

structures. There are, of course, risks in including incompletely characterized

contaminants in the training data set because biases may be introduced in the

mathematical treatment of missing values. However, these risks can be

outweighed by the benefits of having a very large, information-rich training

data set.

Prototype Classification Functions

The simplest function to represent the mapping between contaminant

attributes and the binary classification variable is a linear model:

PCCL TO CCL: CLASSIFICATION ALGORITHM 126

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE 5–1 Correlation plots of the values of the five attributes for

contaminants in the training data set. Crosses represent T=1 contaminants and

circles represent T=0 contaminants.

PCCL TO CCL: CLASSIFICATION ALGORITHM 127

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Y=w0+w1X1+w2X2+w3X3+w4X4+w5X5, (5–3)

where wi is the weight for attribute i. The optimal parameters (i.e.,

weights) are estimated, for example, by minimizing the mse (Equation 5–2).

The linear model is an attractive alternative because the mapping function is

readily understandable and the values of the weights are easy to interpret.

Furthermore, calibrating a linear classification model can be done with very

simple statistical procedures that are widely available. The disadvantage of the

linear model is that its performance is poor if the training data are not “linearly

separable,” as explained later in this chapter. To achieve good performance with

linearly nonseparable data, one must use a model that is capable of nonlinear

dependencies.

Neural networks provide the flexibility to capture linear as well as

nonlinear dependencies (Hornik et al., 1989). Neural networks were originally

conceived in the 1960s and were more formally developed in the 1980s. Their

use has become widespread in the last decade, and a number of excellent

textbooks have been written on the applications of neural networks in a variety

of fields (Garson, 1998; Weiss and Kulikowski, 1990; Zupan and Gasteiger,

1993). Widely cited introductory articles include Bailey and Thompson (1990)

and Hinton (1992).

A neural network is a mathematical representation of a network of

biological neurons. Input data are fed into the network, and output from the

network is computed based on the architecture of the network and the operative

mathematical functions. The simplest neural network is a single neuron with a

vector input and single output (Figure 5–2). In this network, the sum of the

weighted inputs is passed through a transfer function f(). If the transfer function

is linear, then this neural network is mathematically equivalent to the linear

classifier in Equation 5–3. This neuron has a bias that is equivalent to w0, the

intercept in Equation 5–3. More complex neural networks can be constructed by

having multiple layers of neurons, multiple neurons within a layer, complex

connectivity between the layers, and nonlinear transfer functions mapping the

neuron input to the output. For example, a multilayer neural network has

“hidden” layers, which are additional layers of neurons between the input and

output layers.

One of the attractive advantages of using a neural network is that it is not

necessary to specify a priori the mathematical relationship between input and

output data. One chooses the architecture, which specifies the number of

neurons and their organization, and the transfer functions opperative

PCCL TO CCL: CLASSIFICATION ALGORITHM 128

About this P

DF file: This new digital representation of the original work has been recomposed from X

ML files created from the original paper book, not from the

original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be

retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE 5–2 Single-neuron model with a vector input and single output.

at each information node. The more elaborate the architecture, the more

flexible is the model in capturing the functionalities between input and output

and the possibly numerous interactions in variables. It is precisely this

advantage that leads to the primary disadvantage of using neural networks—the

resulting classification algorithm is not readily extracted, and there is a

necessary loss in transparency in exactly how the input variables determine the

classification output.

A cautionary statement is appropriate at this point. In all prototype

classification schemes, the user should be aware of the danger in overfitting the

training data. Overfitting occurs when a modeler has undue confidence in the

precision of the training data set and is overzealous in finding an algorithm that

produces no classification error in representing these data. This can impose

“false structure” on the mapping, which does not truly capture the functional

dependencies. The danger of overfitting is especially present in neural network

modeling because of the tremendous flexibility in the underlying mathematical

relationships. The result is a sacrifice of generalization (predictive) ability. This

issue has been discussed extensively in the literature of statistical learning

theory and information theory (Vapnik, 1995).

The following sections present classification results for the committee’s

illustrative example training data set. The committee presents results for both a

linear classification model and a neural network. The

PCCL TO CCL: CLASSIFICATION ALGORITHM 129

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
classification algorithms were developed using Matlab and the Matlab Neural

Network Toolbox (Mathworks Inc., Natick, Massachusetts). The Matlab

computer codes used for classification, error analysis, and prediction are

included in Appendix B.

Classification Results Using a Linear Classifier

Using the model in Equation 5–3 and the training data set, a linear

regression produced a mean squared error of 0.094. The resulting weights are

listed in Table 5–2. The most important and statistically significant indicators

are severity, potency, and magnitude. This implies that these are the metrics that

have, in the past, determined whether a contaminant is appropriate for

regulatory action. Although in principle prevalence and persistence-mobility are

important indicators of human health hazard, the analysis did not show this.

Either these factors have not been given significant weight in past regulatory

decision making for drinking water safety or one’s ability to estimate these

attribute scores accurately given available data, is poor. This finding may

change when EPA constructs a formal training data set, but it illustrates the type

of conclusion that can be derived.

The resulting distributions of Yi

 values for each of the two types of

contaminants in the training data set are shown in Figure 5–3. Clearly, there is

not a perfect separation between the two categories, which indicates that these

data are not linearly separable. However, there is a clear

TABLE 5–2 Linear Regression Results for Illustrative Example

Attribute Weight Regression Estimate Significantly Different

from Zero at the 95%

Confidence Level?

— w0 0.018 No

Severity w1 0.043 Yes

Potency w2 0.062 Yes

Prevalence w3 `0.029 No

Magnitude w4 0.044 Yes

Persistence-Mobility w5 0.040 No

PCCL TO CCL: CLASSIFICATION ALGORITHM 130

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE 5–3 Histogram of Yi

 values for the training data set using the linear

classifier.

trend that contaminants in the T=0 category tend to have smaller predicted

values of Yi

. This result alone is interesting because it serves to verify that past

regulatory decisions do indeed separate contaminants in a manner consistent

with the factors that the committee believes to be important indicators of human

health risk.

The next step is the determination of an appropriate threshold value that

separates the objects into the two categories. The committee decided to

optimize the value of the threshold by minimizing the total number of

misclassified contaminants. Figure 5–4 is a plot of the number of misclassified

contaminants as a function of the threshold value. Because two values of the

threshold (0.4 and 0.55), produce a minimum in the total number of

misclassified contaminants, the committee employed a secondary criterion that

minimized the sum of the percent errors for each of the categories (see

Figure 5–5). A threshold value of 0.55 produces a smaller percent error in both

categories, so this was chosen as the optimal threshold.

PCCL TO CCL: CLASSIFICATION ALGORITHM 131

About this P

DF file: This new digital representation of the original work has been recomposed from X

ML files created from the original paper book, not from the

original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be

retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE 5–4 Classification error as a function of threshold value, in which

classification error is defined as the number of misclassified contaminants

linear classifier).

FIGURE 5–5 Classification error as a function of threshold value, in which

classification error is defined as the percentage of the number of contaminants

within a category that are misclassified (linear classifier).

PCCL TO CCL: CLASSIFICATION ALGORITHM 132

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
The ability of the classification scheme to separate the training data set is

one way to estimate the classification error that is expected when used for

prediction. With a threshold value of 0.55, the error in misclassifying T=1

contaminants is 8 percent (5 out of 63) and the error in misclassifying T=0

contaminants is 24 percent (4 out of 17). The larger error in misclassifying T=0

contaminants reflects the fact that this portion of the training data set was

smaller, so there is less confidence that the contaminants used are representative

of the population of contaminants in this category. These errors can be

interpreted as follows: If this classification algorithm is used for prediction, 92

percent of the time a contaminant that should be on the CCL will be classified

correctly. This represents the “sensitivity” of the classification. The other

characteristic is the “specificity,” which is that 76 percent of the time a

contaminant that should not be on the CCL will be classified correctly. That is,

the probability of a false negative is 8 percent and the probability of a false

positive is 24 percent. This estimation of errors is essentially a demonstration of

what the committee discussed in Appendix A of its first report (NRC, 1999a).

Classification Results Using a Neural Network Classifier

The neural network used for this classification problem has two layers

connected in a feed-forward configuration (Figure 5–6). The first is a hidden

layer containing two nodes (neurons). A hyperbolic tangent sigmoidal function

was chosen as the transfer function for each node in this layer. This transfer

function is one of several commonly used nonlinear functions. The second is

the output layer with a single node and a linear transfer function. All nodes have

biases. Other possible network architectures could have been used for this

problem (e.g., additional nodes in the hidden layer or different transfer

functions). The committee chose this architecture because it is one of the

simplest but has sufficient flexibility to capture nonlinear dependencies.

The training algorithm used to calibrate the neural network was a

conjugate gradient method that constitutes a family of efficient search

algorithms. This search method converges much faster than the simpler gradient

descent methods, but as is true with all nonlinear optimization problems, care

must be taken to ensure robustness in the solution by running several training

sessions with different initial conditions. Network

PCCL TO CCL: CLASSIFICATION ALGORITHM 133

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
performance is measured according to the minimum of mse (Equation 5–2).

Using the committee’s training data set, the resulting mean squared error

was 0.018, which is considerably smaller than that achieved using the linear

classifier. This clearly demonstrates the improvement in fitting capabilities of a

neural network model over a linear model. The classification results using the

neural network model are shown in Figure 5–7. The enhanced ability of the

neural network classifier to separate the training data is clearly seen by

comparing the histograms in Figures 5–3 and 5–7. The improved performance

of the neural network is rather remarkable considering that the neural network

architecture (Figure 5–6) is one of the simplest possible for this problem.

Classification error analysis indicated that the optimum threshold value of Y for

the neural network classifier is 0.55, which coincidentally is the same as the

resulting optimal threshold for the linear classifier. The predicted classification

error for the neural network classifier is 3 percent for false negatives (2 out of

63). Because there were no misclassifications of the T=0 contaminants, the falsepositive rate is estimated to be near zero.

FIGURE 5–6 Multilayer neural network used for contaminant classification.

Weights are not shown, for simplicity.

PCCL TO CCL: CLASSIFICATION ALGORITHM 134

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE 5–7 Histogram of Yi values for the training data set using the neural

network classifier.

For the linear classifier it was possible to examine the values of the

weights and their statistical significance to determine the relative importance of

the different attributes in determining the classification outcome. Because of the

greater complexity of the neural network model, there is less transparency in the

functional mapping. In most computer programs, the weights can be examined,

but it is very difficult to extrapolate these weights mentally to readily

understandable rules. For example, in this neural network, there are 10 weights

in the first layer, 2 weights in the second layer, and a total of 3 bias weights.

Because these parameters relate to cross-products and nonlinear functions of the

attributes, it is not immediately obvious what the values mean. One way to

probe the importance of various attributes in a neural network is to leave out

certain attributes in the training data set and examine the effect on the

classification of the training data. Indeed, this practice is discussed in Chapter 2

as a means to enhance the transparency of the approach, and the committee

recommends that EPA conduct such a sensitivity analysis on any classification

model it develops and intends to use to assist in the creation of future CCLs.

PCCL TO CCL: CLASSIFICATION ALGORITHM 135

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
DEMONSTRATED USE OF THE TRAINED CLASSIFIER

Examination of Misclassified Contaminants

Misclassification of contaminants in the training data set can be interpreted

in three ways: Either (1) the training data (i.e., the attribute scores) do not

capture the information that determines regulatory action for drinking water

contaminants; (2) the model relating the input to the output does not adequately

capture the process by which this information is used in regulatory decisionmaking; or (3) the target values are wrong, implying that some of the regulatory

decisions made in the past are inconsistent with regulatory decisions for the

bulk of drinking water contaminants. If one operates on the presumption that

the training data are accurate, precise, and complete, then classification errors

can be reduced by exploring modeling alternatives. That is precisely what was

done in the analysis in this chapter. A large number of classification errors

resulted from the linear model, so a neural network model was used that greatly

reduced classification error. One can continue to try to eliminate all

classification errors by using more and more elaborate neural networks (or other

classification models such as support vector machines, cluster algorithms, radial

basis functions). However, as mentioned previously, this endeavor may lead the

analyst down the potentially dangerous path of overfitting the data. Thus, an

analyst must question whether persistent errors are indicative of true

classification errors.

If one operates on the assumption that the attribute scores should combine

in a linear fashion to determine classification output, then some insights can be

gained by examining which of the contaminants in the training data set were

misclassified using the linear classifier. These are listed in Table 5–3. Total

coliforms and heterotrophic plate count (HPC) are misclassified because these

contaminants are not inherently hazardous, but rather are indicators of the

potential presence of hazardous microorganisms. For this reason, the committee

believes it is appropriate for these two contaminants to fall below the threshold.

The linear classifier also misclassified toluene, o-dichlorobenzene, and

trans-1,2-dichloroethylene, which are currently regulated chemicals. These

chemicals score relatively low on severity and potency because their health

effects are smaller than those of other regulated drinking water contaminants

that are of greater concern possibly because of their carcinogenic potential or

their potential to cause health effects at low

PCCL TO CCL: CLASSIFICATION ALGORITHM 136

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 5–3 Contaminants in the Training Data Set That Were Misclassified Using

the Linear Classifier

Misclassified T=1 Contaminants Yi Misclassified T=0 Contaminants Yi

o-Dichlorobenzene 0.47 Ethanol 0.57

trans-1,2-Dichloroethylene 0.54 Folic acid 0.72

Toluene 0.43 Olestra 0.63

HPC 0.28 Saccharin 0.70

Total coliforms 0.42

concentrations. Toluene is a chemical that is generally known to be rather

prevalent, but the occurrence data available were insufficient to represent this

fact. In general, the data used to score prevalence for all contaminants in the

training data set were found to be lacking in several respects (see Chapter 4).

These misclassifications may be indicative of a paucity of good information on

which to base occurrence metrics. Of the T=0 contaminants that were

misclassified in the linear classifier, the committee notes that ethanol, folic acid,

and saccharin have potentially severe health impacts, and Olestra has significant

potential for persistence and mobility in the environment. Like other substances

in the T=0 category, however, these are substances that many people

purposefully and regularly ingest.

Rather than placing emphasis on the misclassifications obtained using the

linear classifier, one can operate on the assumption that the underlying process

by which the attribute scores determine classification output is complex and

requires nonlinear functionalities to represent it mathematically. This makes the

case for the use of neural networks rather than linear classifiers. The

misclassified contaminants for the neural network classifier are listed in

Table 5–4. The neural network classifier had a much smaller number of

misclassified contaminants, indicating that it was able to capture the data

mapping process mathematically. It is interesting to note that the misclassified

contaminants in this case are not a subset of those misclassified in the linear

classification. Here, ethylbenzene has been misclassified, whereas it had not

been misclassified in the linear classification. This indicates the strong

underlying nonlinearity that is being modeled by the neural network.

PCCL TO CCL: CLASSIFICATION ALGORITHM 137

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 5–4 Contaminants in the Training Data Set That Were Misclassified Using

the Neural Network Classifier

Misclassified T=1 Contaminants Yi Misclassified T=0 Contaminants

Ethylbenzene 0.50 —

HPC 0.08 —

Validation Test Cases

The contaminants in the training data set in the T=1 category did not

include all those that have MCLs. Five such chemical and microbial

contaminants were withheld as validation test cases to examine the predictive

accuracy of the classification algorithm as required in the second phase of study

(see Preface to this report). Details of how health effect and occurrence

attributes were scored for these regulated contaminants are discussed earlier in

this chapter. The predicted values of Yi

 for the five validation test cases are

listed in Table 5–5. All contaminants are correctly classified as being in the T=1

category because their Yi values are well above the thresholds. These correct

predictions, albeit few in number, provide additional supporting evidence of the

validity of the classification algorithm. EPA should make every effort to

increase the number of both types of validation test cases (especially for T=0

contaminants) to assess more thoroughly the predictive accuracy of any

classification algorithm developed for use in the creation of future CCLs.

Prediction for Interesting Test Cases

The committee selected five potential drinking water contaminants for

which data were available that may be of interest in the future (Table 5–6). Two

of these, aluminum and methyl-t-butyl ether (MTBE), are currently included on

the 1998 CCL (EPA, 1998b). The linear classifier predicts that all five should

be considered for regulatory action. However, the neural network classifier

predicts that silver, chloroform, aspirin, and MTBE should be placed on the

CCL, but aluminum should not (based on data available at this time). The

committee emphasizes that these results do not indicate a recommendation that

any of these contaminants

PCCL TO CCL: CLASSIFICATION ALGORITHM 138

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 5–5 Classification Prediction Accuracy for Validation Test Cases

Validation Test Cases Predicted Yi

 Using Linear

Classifier (Threshold =

0.55)

Predicted Yi Using Neural

Network (Threshold = 0.55)

Arsenic 1.2 1.00

Nitrate 1.1 1.00

Atrazine 0.79 1.00

Tetrachloroethylene 0.76 0.99

Giardia lamblia 1.1 1.00

TABLE 5–6 Classification Prediction for Interesting Test Cases

Interesting Cases Predicted Yi Using Linear

Classifier (Threshold=0.55)

Predicted Yi Using Neural

Network (Threshold=0.55)

Aluminum 0.61 0.08

Silver 1.2 1.00

Chloroform 0.8 1.00

Aspirin 1.0 1.00

MTBE 0.98 1.00

be placed on a future CCL or removed from the 1998 CCL. Rather, the

results are intended to demonstrate how the EPA can develop and use a

classification scheme to help select PCCL contaminants for inclusion on the

CCL. The committee also believes that this demonstrates the sensitivity of the

results to choices made in constructing the classification algorithm.

SUMMARY: CONCLUSIONS AND RECOMMENDATIONS

The purpose of this analysis was to demonstrate how existing contaminant

data and past regulatory decisions could be used to develop a prototype

classification algorithm to determine, in conjunction with expert judgment,

whether a particular drinking water contaminant is of regulatory concern or not.

More specifically, the committee has demonstrated

PCCL TO CCL: CLASSIFICATION ALGORITHM 139

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
a prototype classification approach that must first be trained (calibrated) using a

training data set containing prototype contaminants and can then be used in

conjunction with expert judgment to predict whether a new (PCCL)

contaminant should be placed on the CCL or not. The use of the majority of

currently regulated drinking water contaminants in the training data set can be

described simply as “making decisions that are consistent with and build upon

what has been done in the past.” If this is deemed defensible, the prototype

classification approach can be regarded as valid. It is important to realize that

this approach is then constrained by the data (i.e., past regulatory decisions).

What has been presented in this chapter is a framework and demonstration

of how EPA might develop its own prototype classification scheme for use in

the creation of future CCLs. In this regard, the committee makes the following

recommendations:

• A linear model and a neural network are discussed and demonstrated for

potential use in a prototype classification scheme. Although the neural

network performed better than the linear model (with respect to minimizing

the number of misclassified contaminants), at this time the committee

cannot make a firm recommendation as to which model EPA should use

because of the aforementioned uncertainties in the training data set. Thus,

the committee recommends that EPA explore alternative model

formulations and be cognizant of the dangers of overfitting and loss of

generalization.

• To adopt and implement the recommended approach for the creation of

future CCLs, EPA will have to employ or work with persons

knowledgeable of prototype classification methods and devote appreciable

time and resources to develop and maintain a comprehensive training data

set. In this regard, the committee recommends strongly that EPA greatly

increase the size of the training data set that is used illustratively in this

chapter to improve predictive capacity. One way in which EPA can expand

the training data set and classification algorithm is to allow for the expected

case of missing data—that is, to purposefully include in the training data

set contaminants for which values of some of the attributes are unknown

and develop a scheme that allows prediction of contaminants for which

some of the attributes are unknown.

• EPA will also have to accurately and consistently assign attribute scores for

all contaminants under consideration. To do this, it will have to collect and

organize available data and research for each PCCL contaminant and

document the attribute scoring scheme used to help ensure

PCCL TO CCL: CLASSIFICATION ALGORITHM 140

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
a transparent and defensible process, the importance of which is discussed

in Chapter 2. As recommended in Chapter 3, the creation of a consolidated

database that would provide a consistent mechanism for recording and

retrieving information on the contaminants under consideration would be of

benefit.

• EPA will also have to withhold contaminants from inclusion in the training

data set to serve as validation test cases that can assess the predictive

accuracy of any classification algorithm developed. While the committee

was able to withhold five contaminants presumed worthy of regulatory

consideration (T=1) for this purpose (see Table 5–5), it had insufficient

numbers of contaminants presumed not worthy of regulatory consideration

(T=0) to similarly withhold. All withheld validation contaminants were

classified correctly as belonging in the T=1 category and such results

provide (albeit limited) additional support for the validity of the

classification algorithm approach. EPA should make every effort to

increase the number of both types of validation test cases (especially for

T=0 contaminants) to assess more thoroughly the predictive accuracy of

any classification algorithm developed for use in the creation of future

CCLs.

• If neural networks are used for prototype classification, the transparency in

understanding which contaminant attributes determine the category of a

contaminant will be less than that of a linear model or a more traditional

rule-based scheme. However, if one acknowledges that the underlying

process that maps attributes into categorical outcomes is very complex,

then there is little hope that an accurate rule-based classification scheme

can be constructed. The fact that the nonlinear neural network performed

better than the linear classifier is a strong indicator that the underlying

mapping process is complex, and it would be a difficult task for a panel of

experts to accurately specify the rules and conditions of this mapping.

Furthermore, the loss in transparency in using a neural network is not

inherent, but rather derives from the difficulty in elucidating the mapping.

• The underlying mapping in a neural network classifier can be examined just

as one would conduct experiments to probe a physical system in a

laboratory. Through numerical experimentation, one can probe a neural

network to determine the sensitivity of the output to various changes in

input data. Although a sensitivity analysis was not conducted because of

time constraints, the committee recommends that EPA should use several

training data sets to gauge the sensitivity of the method as part of its

analysis and documentation if a classification approach

PCCL TO CCL: CLASSIFICATION ALGORITHM 141

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
is ultimately adopted and used to help create future CCLs.

• Finally, EPA should realize that the committee is recommending a

prototype classification scheme to be used in conjunction with expert

judgment for the future selection of PCCL contaminants for inclusion on a

CCL. Thus, transparency is less crucial (though no less desired) at this

juncture than when selecting contaminants from the CCL for regulatory

activities as discussed in the committee’s first report (NRC, 1999a).

PCCL TO CCL: CLASSIFICATION ALGORITHM 142

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
6

Virulence-Factor Activity Relationships

INTRODUCTION

The term “virulence-factor activity relationship,” or VFAR (formerly

referred to as virulence-activity relationship or VAR; NRC, 1999a), is rooted in

a recognition of the utility of using structure-activity relationships (SARs) to

compare the structure of newly identified or produced chemicals to known

chemical structures to enable prediction of their toxicity and other physical

properties. In essence, the committee believes the same principle can be applied

to waterborne pathogens. It is important to state that many sections of this

chapter necessarily include more extensive use of scientific terms and language

than might typically be found in the body of a National Research Council

(NRC) report. That is, rather than deleting, simplifying, or relegating such

relevant technical language to an appendix, the committee decided to keep all

information related to VFARs in one comprehensive chapter. This chapter

should be read with that qualification in mind.

For microorganisms, there are many levels of structure, such as the cell or

organism itself and the larger internal components that comprise the

microorganism (e.g., nucleus, micronemes, flagellae). These morphological

components can sometimes be used to identify pathogenic microorganisms.

Beyond these relatively large structures, there are smaller, biochemical

components of the organism, including proteins, carbohydrates, and lipids.

Many of these biochemical building blocks are directly related to how a

particular microorganism causes disease. Some examples of these include the

outer coat of some bacteria (the lipid polysaccharide coat), attachment and

invasion factors, and bacterial toxins. Thus, the central premise of VFARs is to

relate the architectural and biochemical components of microorganisms to

potential human disease.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 143

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Virulence can be defined as the quality of being poisonous or injurious to

life (i.e., virulent). For an organism to be virulent, it must be able to infect its

human host, reproduce, and/or cause a disease. This broad definition of

virulence is more inclusive than the narrow definition commonly used by

microbiologists (i.e., virulence is solely the severity of the disease produced

after exposure and infection). Each of the microbiological attributes that

contribute to virulence can in general be linked to specific architectural

elements or biochemical compounds within the organism. Together, these

elements and compounds can generally be termed “virulence factors,” and the

blueprints for them are included in the genetic code of an organism. For this

reason, a principal topic of this chapter is the genetic structure of various

microorganisms because of its direct relationship to virulence factors.

Owing to recent advances in molecular biology, the genetic structures of

many thousands of organisms (especially bacteria and viruses) have been

identified, reported, and stored in what are called gene banks. Sophisticated

computer programs allow for the sorting and matching of genetic structures and

specific genes. The discipline that organizes and studies these genes is known

as bioinformatics, while the study of genes and their function is known as

genomics. In addition, a growing area of related interest is functional genomics,

that is, understanding the specific role of genes in terms of the function of the

organism. The ability to use these and related tools to address the microbial

contamination of drinking water is illustrated by some of the following

observations:

• The genetic structures of most known waterborne pathogens have been

characterized at least partially, with the information stored in gene

databanks. The complete genome of several important waterborne

pathogens, such as Vibrio cholerae (the agent of cholera), is now known,

and many more will be characterized in the near future (Heidelberg et al.,

2000).

• Other related information is accumulating that allows the use of these

databanks to determine or predict the ability of a microorganism to produce

virulence factors, such as toxins, attachment factors, and other surface

proteins, and genes that encode bacterial resistance to antibiotics.

• On a more basic level, these data can be used to characterize similarities

and differences between a microorganism of interest and known pathogens.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 144

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
• Data of this kind can also be used to identify sources of, and thus exposures

to, microorganisms through molecular “fingerprinting.”

• The functional genomics or bioinformatics expertise needed to establish a

nationwide VFAR program already exists in the private and public sectors.

Thus, the committee concludes that a VFAR concept, with many parallels

to the SAR concept used for chemicals, would be a powerful approach to

examining emerging waterborne pathogens, opportunistic microorganisms, and

other newly identified microorganisms.

STATEMENT OF THE PROBLEM

As noted in the committee’s first report (NRC, 1999a), the current

approach to identifying and controlling waterborne disease is limited. It has

followed a similar path since cholera was first linked to transmission via water

(“from the Broad Street pump”) in London, England, nearly a century and an

half ago, and since Koch first proposed his famous postulates regarding

causation (see Okun, 1999). Typically, a disease outbreak is reported only when

a significant portion of a community is recognized to have been affected, the

responsible microorganism has been identified, and an epidemiological study is

undertaken to determine possible sources of exposure to the agent in the

community. If any of these three elements is lacking, the outbreak is generally

missed and goes unreported. If the consumption of drinking water is identified

as a potential source of exposure, a public health advisory to boil water may be

issued. Alternatively, the culpable part of the system may be identified and

isolated until the cause of contamination is eliminated. However, for most of the

waterborne outbreaks in the United States, the etiology is never determined, the

responsible microorganism is never identified, and public water systems are not

easily fixed or shut off. The identification of pathogens is thus unnecessarily

related to the recognition of an outbreak. Under the amended Safe Drinking

Water Act, microbial contamination, regardless of whether it is associated with

an outbreak or not, must be addressed.

Hundreds if not thousands of microorganisms have the potential to be

spread through drinking water supplies and distribution systems. While data on

health effects for many of these are described in the medical literature, there are

no occurrence databases or even routine methodologies for developing these

databases (NRC, 1999a). One of the principal

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 145

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
dilemmas to be addressed is that current regulatory practice requires that

methods to culture organisms of interest be developed before occurrence data

can be gathered. Thus, a microorganism ordinarily must first be identified as a

pathogen, and be capable of in vitro culture, before occurrence data are

acquired. This long-standing paradigm makes it very difficult or impossible to

develop a database of potential or emerging pathogens.

There is also no widely accepted approach for prioritization of waterborne

pathogens, other than through expert judgment. For example, and as noted

earlier, the U.S. Environmental Protection Agency (EPA) and the American

Water Works Association Research Foundation have jointly sponsored a series

of expert workshops since 1996 for the development of a decision process for

prioritizing emerging waterborne pathogens that is nearing completion. These

expert judgments must be made, of necessity, by a very small number of

researchers in the discipline of health-related environmental microbiology. This

approach to the process makes transparency very difficult to achieve, the

importance of which is discussed in Chapters 2 and 5 of this report.

The committee believes strongly that if EPA continues to rely on exposure

and health effects as two primary data categories for screening potential

microbial drinking water contaminants, progress will continue to be

unacceptably slow. Current efforts are able to address only one or two

microorganisms every 5 to 10 years with the current CCL development and

implementation approach. To illustrate the dilemma, consider that of the ten

microorganisms and groups of related microorganisms on the 1998 CCL, nine

are in the “research priorities” category (see Table 1–3) and will go unregulated

in the first CCL cycle. Of these nine microbial contaminants, only one,

Aeromonas hydrophila, is slated for delayed screening level monitoring during

the first cycle of the Unregulated Contaminant Monitoring Regulation (UCMR)

(EPA, 1999c) (see Table 1–4).

It is clear that a severe bottleneck exists in identifying and addressing

important microbial contaminants in drinking water. Thus, a new approach to

assessing pathogens could help overcome this ongoing problem.

VFAR ANALOGY TO SARS AND QUANTITATIVE SARS

A variety of terminology has developed in the literature to identify various

classes of correlations useful for predicting the properties of

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 146

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
agents in environmental and health sciences.

For example, chemical properties are amenable to prediction through use

of structure-activity relationships, which can be distinguished from propertyactivity relationships (PARs) and structure-property relationships (SPRs).

Although careful classification along these lines certainly has heuristic value

(e.g., Brezonik, 1990), few researchers adhere to these distinctions rigorously.

Instead, only a few terms are commonly used and these are often applied to a

wider range of correlation types than strict use of each expression would allow

(Tratnyek, 1998). One example of this is the term linear free energy relationship

(LFER), which originally referred to a specific type of correlation used by

physical organic chemists but eventually came to represent the entire field of

correlation analysis in organic chemistry (Shorter, 1973). Similarly, the term

quantitative structure-activity relationship (QSAR) was originally coined for

use in drug design but is now commonly used to refer to many types of

correlations employed in the pharmaceutical, toxicological, and environmental

sciences. By analogy to the above discussion, the committee has coined the

term “virulence-factor activity relationship” and defined it as the known or

presumed linkage between the biological characteristics of a microorganism and

its real or potential ability to cause harm (pathogenicity).

FRAMEWORK

The central concept is to use microbial characteristics to predict virulence

via what the committee terms a virulence-factor activity relationship. Microbial

VFARs would function in much the same way as QSARs do, namely to assist in

the early identification of at least several potential elements of virulence.

Research increasingly has shown certain common characteristics among

virulent pathogens, such as the production of specific toxins, specific surface

proteins, and specific repair mechanisms that enhance their ability to infect and

inflict damage in a host. Recently some of these “descriptors” (the terminology

often used in QSARs) have been tied to specific genes, and it has become

evident that the same can be done for others. Identification of these descriptors,

either directly or through analysis of genetic databases, could become a

powerful tool for estimating the potential virulence of a microorganism. This is

particularly true for two important aspects of virulence: potency and persistence

in the environment. The committee conceives of VFAR

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 147

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
as being the relationship that ties specific descriptors to outcomes of concern

(see Figure 6–1).

FORMULATING VFARS

Conceptually, pathogens of interest must be related in that they exhibit

pathogenicity through a common mechanism but are also likely to be

distinguished through secondary characteristics that cause virulence to be

variable. Since virulence is the target property to be predicted by the VFAR, it

is by definition the dependent or “response” variable in a VFAR. Variability in

the virulence of pathogens may be characterized by one or more independent

variables (i.e., variability in the genetic makeup) —referred to as “descriptor

variables” —that can be conveniently

FIGURE 6–1 Schematic drawing of VFAR predicting outcomes of concern

(virulence, potency, persistence) using the presence or quality of “descriptor”

variables.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 148

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
measured or otherwise determined. When correlation of the response and

descriptor variables yields a consistent relationship, the result can be used as a

(quantitative) model for comparing and predicting properties of related

pathogens.

Note that there are many potential ways in which response and descriptor

variables may be defined, and this gives flexibility to the VFAR approach, such

that it should in principle be able to accommodate many complicating factors.

For example, when variability in a pathogen’s virulence is related substantially

to host factors (e.g., when the host is in an immuno-compromised state) then an

“interaction effect” could generate cases that do not obey a VFAR. However, if

the response variable (virulence) is defined in such a way that it is unaffected by

the behavior of opportunistic pathogens, or if descriptor variables are used that

incorporate opportunistic behavior, then a VFAR can incorporate this effect and

outliers can be avoided. Such subtleties suggest that developing and validating

robust and reliable VFARs will require considerable research, but the

committee believes that the promise of VFARs should make them a high

priority for such research.

Response (Outcome) Variables

As noted previously, the response variable of concern in VFARs is

virulence. Narrowly defined, pathogenicity can be characterized as the ability to

cause disease and clinical virulence as a measure of the severity of disease. A

broader definition is used in this report, where virulence (with respect to

VFARs) incorporates both the concept of pathogenicity and the narrower

concept of clinical virulence. Viewed in this manner, it may be useful to include

attributes of persistence in the environment as contributing to virulence. It is

also conceivable that pathogenicity, clinical virulence, and environmental

persistence could be considered separate response variables that work together

to contribute to the broadly defined “virulence” of a pathogenic organism.

There are a number of potential metrics of virulence (broadly defined) that

may be used as a quantitative outcome measure. These include the duration of

symptomatic illness and the intensity of symptoms (perhaps using a disabilityweighted scale).

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 149

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Descriptor Variables

Descriptor variables, in this context, are those attributes of a

microorganism that may prove useful in predicting their virulence. For example,

the presence of toxins, adherence factors, adhesins, invasins, capsular

components, fimbria, hemolysins, metabolic pathways, and antibiotic resistance

could prove to be effective descriptors of microbial virulence. Alternatively,

association with certain families of pathogenic microorganisms may be

sufficient as a descriptor (e.g., for viruses), and species and genotype may be all

that is necessary for protozoa. As our knowledge of pathogens improves, the

definition and calibration of specific descriptors will evolve as well.

For many pathogens, the specific mechanisms or virulence descriptors that

underlie the range of virulence from one genotype to the next are not well

understood. Because of this circumstance, it has already been demonstrated in

waterborne pathogens that a genetically based VFAR approach could be

particularly powerful. For example, recent studies suggest that various isolates

or species of Cryptosporidium are virulent to varying degrees in humans (e.g.,

Okhuysen et al., 1999; Widmer et al., 1998). The ability to recognize and

differentiate the genomic content of these different isolates or species, and thus

recognize differences in virulence, is based upon the same intellectual concepts

that underlie the recognition of toxin-encoding bacterial genes. The power of a

VFAR approach is that it has the ability to genuinely reflect the true biological

diversity found in human pathogens, even when the exact mechanisms that

shape this diversity are not yet understood (Morgan et al., 1999a; Sulaiman et

al., 2000).

The committee anticipates that the VFAR paradigm is robust enough to

accommodate the reality that sometimes the mere presence of a protozoan in

drinking water is not of public health concern. For example, there is now

abundant evidence that the species Cryptosporidium parvum is, in fact, made up

of a number of genotypes, each with different virulence where the human

population is concerned (Xiao et al., 2000). Furthermore, one study (Morgan et

al., 1999a) used genetic methods to identify eight different species of

Crytposporidium: parvum (many mammals), muris (rodents, cattle), felis (cats),

wrairi (guinea pigs), meleagridis and baileyi (birds), serpentis (reptiles), and

nasorum (fish). The same study demonstrated seven genotypes of parvum:

genotype 1 infects humans only; genotype 2 infects cattle, sheep, goats, and

humans; genotype 3 infects mice and bats; genotype 4 infects pigs; genotype 5

infects koalas and kangaroos; genotype 6 infects dogs; and genotype 7

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 150

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
infects ferrets. Subsequent studies indicate the existence of an eighth genotype

(Sulaiman et al., 2000). Of these, only genotypes 1 and 2 are believed to infect

immuno-competent humans, but several genotypes have been found to infect

immuno-compromised individuals (Morgan et al., 1999b). In addition, several

non-parvum species (C. felis, C. meleagridis) have been found to infect people

with AIDS (Morgan et al., 2000).

Genomics and Proteomics

Proteomics, a discipline within functional genomics, is the study of protein

sets made (expressed) when the genomic blueprint of an organism is actually

translated into functional molecules. When faced with changing environmental

conditions, organisms will respond by making different sets of proteins to help

them survive. For example, it has been estimated that Vibrio cholerae is capable

of making approximately 3,900 different proteins depending on environmental

conditions (Heidelberg et al., 2000). These proteins are the actual molecules

that build other important structural molecules, such as lipids, deoxyribonucleic

acid (DNA), and ribonucleic acid (RNA), and are capable of having both

structural and catalytic or enzymatic functions. It is known that some important

bacterial toxins (such as Shiga toxin, discussed later in this chapter) are

maximally produced under very specific conditions (Acheson et al., 1991).

Faced with a hostile environment, many bacteria will shift production of a

protein set that is associated with growth to another set associated with a viable

but nonculturable state or to the formation of spores as discussed later in this

chapter. Thus, knowledge of the set of proteins being made by an organism can

impart information far more revealing that that gained from studying the

expression of a single protein.

The committee anticipates that because the state of the art of genomics is

currently more advanced than that of proteomics, the initial emphasis in VFAR

formulation will be genetic. While much is already known about the growing

field proteomics, the committee believes it would be premature to discuss or

make recommendations about how much research and data will be needed to

examine this aspect of developing VFARs, particularly under changing

environmental conditions. Nonetheless, the logical extension of identifying and

understanding the entire genome of an organism is ascertaining how this is

translated into the expression of proteins and other structural building blocks. In

this

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 151

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
regard, the committee anticipates that the same rationale that exists for using

genomics also exists for proteomics. For example, in a subsequent section of

this chapter the committee discusses the use of DNA chips that act as sensors

for finding the characteristic DNA elements that encode a particular virulence

factor. These chips function via a binding interaction between a section of DNA

spotted onto the chip and the complementary strand of a target DNA molecule,

such as one from a pathogenic organism. Protein chips that bind the actual

virulence protein factors could work in an analogous fashion. Under such a

scenario, binding molecules known to attach to specific bacterial toxins (e.g.,

monoclonal antibodies) could be spotted onto a chip and used to sift through the

proteins expressed by a novel bacterium to see if a protein of concern is made

by the targeted organism.

CURRENT LEVEL OF GENETIC CHARACTERIZATION

In this section, three existing, major bodies of endeavor that have

relevance to the development and implementation of VFARs are discussed.

Microbial Genome Projects and Comparative Databases

The first major endeavor to be discussed in this section is the set of singleorganism genome projects and the large genomic databases that are used for

comparing the genes of one organism with those of others. The genome projects

are comprehensive attempts to sequence the entire genomes of organisms, such

as yeast, pathogenic microorganisms, and humans. Computerized analysis and

the growing use of automated polymerase chain reaction (PCR) techniques have

allowed for tremendous gains in the study of microbial genomics as well as of

whole organisms. The databases that exist to store such information are large

and expanding daily. For example, the Institute for Genomic Research (TIGR)

maintains a collection of databases containing DNA, protein and gene

expression, and taxonomic data for microbes, plants, and even humans (see

http://www.tigr.org for further information). TIGR also provides links to

worldwide genome sequencing projects.

A number of microorganisms are listed in Table 6–1 whose genomes have

already been studied; the results of much of this work are available in the

published literature. A number of these organisms are associated

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 152

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE 6–1 Examples of Microbial Genome Databases for Waterborne Pathogens

Microorganism Size (Million Base Pairs)

Campylobacter jejuni 1.7

Encephalitozoon cuniculi 2.9

Enterococcus faecalis 3.0

Escherichia coli 4.6

Giardia lamblia 12

Helicobacter pylori 1.66

Klebsiella pneumoniae

Legionella pneumophila 4.0

Leptospira interrogans serovar icterohaemorrhagiae 4.8

Mycobacterium avium 4.7

Pseudomonas aeruginosa 5.9

Salmonella paratyphi A 4.5–4.8

Salmonella typhi

Salmonella typhimurium

Shigella flexneri 4.7

Vibrio cholerae 4.0

SOURCE: TIGR (see http://www.tigr.org).

with waterborne disease. For example, studies on the Giardia genome have

recently been published (Adam, 2000), and the complete sequence of Vibrio

cholerae was recently announced with great acclaim (Heidelberg et al., 2000).

The Cryptosporidium genome is being sequenced by investigators at the

University of Minnesota (http://www.cbc.umn.edu/ResearchProjects/AGAC/Cp/

index.htm), with other important work being conducted in the United Kingdom

(http://www.mrclmb.cam.ac.uk/happy/CRYPTO/Ref.html), California (http://

medsfgh.ucsf.edu/id/CpTags/), and elsewhere. Notably, funding for the Vibrio 

cholerae and Cryptosporidium genome projects was provided by the National

Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes

of Health (see http://www.niaid.nih.gov/dmid/genomes/genome.htm for a

listing of genome projects currently supported by NIAID).

On May 30,2000, an important report entitled Interagency Report on the

Federal Investment in Microbial Genomics was published by the Biotechnology

Research Working Group—a subcommittee of the National Science and

Technology Council (BRWG, 2000). The charge for

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 153

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
the Subcommittee on Biotechnology was to summarize the activities in

microbial genetics of a number of federal agencies; identify each federal

agency’s areas of interests; and identify opportunities for, and limitations to,

research in microbial genetics.1 One of the main findings of this report is that

the current effort in microbial genomics in each federal agency is based on the

mission of the specific agency, and, thus, there are clear research gaps—or

opportunities for cooperation—in the area of microbial genomics.

Notably, despite EPA’s clear mandate for the surveillance of water

supplies and developing a rational and transparent scheme for regulating

pathogenic water contaminants to date, it has not yet participated in these

interagency genome project efforts. Indeed, the research gaps and opportunities

identified in this important report (BRWG, 2000) did not even mention the field

of waterborne pathogens, possibly because the participating federal agencies do

not address this particular area of public health and EPA has not yet participated

in this forum. However, the report did identify the problem of pathogens that

are difficult to culture—the importance of which is discussed elsewhere in this

chapter—high-lighting the potential for synergism between federal agencies. In

this regard, the committee strongly recommends the future participation of EPA

in such cooperative interagency programs.

The U.S. Department of Energy (DOE) has been involved extensively in

building genetic databases on microorganisms. Although much of DOE’s focus

has been on bioremediation and not on waterborne pathogens per se, the

methodologies for building, using, and interpreting genetic databases are

applicable (DOE, 2000). More specifically, much of DOE’s research in this

field has recently turned to address functional genomics. As noted previously,

breakthroughs in genome sequencing, along with characterization of proteins

through use of supercomputers, make this possible.

The uses of large genetic databases are many, but most important to this

report is their role in identifying similar genes—and thus virulence

1 Agencies that contributed to this report include the Departments of Defense,

Agriculture, and Energy; the National Aeronautics and Space Administration; the

National Institutes of Health; the National Institute of Standards and Technology; and the

National Science Foundation. Within the National Institutes of Health, the National

Centers for Research Resources, Human Genome Research Institute, National Institute

of Allergy and Infectious Diseases, Dental and Cranial Research, and General Medical

Sciences, as well as the National Library of Medicine’s National Center for

Biotechnology Information, participated.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 154

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
factors—in different organisms. As discussed later in this chapter, many wellrecognized waterborne pathogens share the same or very similar genes that

encode virulence factors. However, it is not possible to predict all of the

insights that may result from the use of these databases. To illustrate this point,

when the genome of Vibrio cholerae was compared to other organisms in the

database (Heidelberg et al., 2000), the largest number of similarities was found

to Escherichia coli (the common enteric pathogen), but the second largest

number was unexpectedly to Haemophilus influenzae (a respiratory pathogen).

Although the latter is not known to be transmitted via water, this finding aptly

demonstrates the surprising ability of pathogens to share common components

that may or may not relate to overall virulence.

Molecular Methods for Characterization of Waterborne

Microorganisms

The second major arena of endeavor to be discussed is the use of

molecular methods, particularly PCR, for the detection and characterization of

waterborne pathogens (NRC, 1999a; Wiedenmann et al., 1998). Perhaps

because the recognized and known bacterial pathogens are culturable, the major

recent focus has been on microorganisms that are not yet culturable (e.g.,

protozoan agents, certain viruses). In this regard, PCR is an attractive diagnostic

procedure because it is rapid, sensitive, and pathogen specific (DiGiovanni et

al., 1999; Johnson et al., 1995; Kostrzynska et al., 1999; Rochelle et al., 1997b).

Independent of this report, Rochelle et al. (1997b) have recommended that PCR

should become a more widely accepted method of pathogen detection and

monitoring within the water industry and should be used in parallel with

conventional techniques (e.g., cultivation) to improve detection capabilities for

existing and newly emerging pathogens.

Although it is beyond the scope of this report to discuss genetics in a

substantive way, a very concise overview of molecular genetics as related to

virulence factors is appropriate. All living organisms carry with them a

complete genetic code, the genome, which serves as a master blueprint for all

cellular structures and activities for the lifetime of the cell or organism. An

organism’s genome consists of DNA and associated protein molecules, except

for RNA viruses, which use RNA rather than DNA. Each DNA molecule

contains many genes—the basic functional units of heredity and what makes

each organism individually distinct. Genes in turn are comprised of specific

sequences of four nucleotide

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 155

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
bases (adenine, thymidine, cytosine, and guanine represented as A, T, C, and G,

respectively). Each strand of DNA is bound to a complementary strand of DNA.

Every individual base in one DNA strand is matched in the complementary

strand by the base that binds to it to form a base pair (bp). These pairs of DNA

bases are formed from As and Ts or from Gs and Cs (see Figure 6–2). Copies of

individual genes are made by an organism by separating the two strands, and

then making a new complementary strand of DNA using one strand as a

template. By convention, an organism’s genome size is expressed as the total

number of base pairs (see Table 6–1). Some organisms use slightly different

nucleotides, but the principle is the same. Genes specify the exact genetic

instructions required to create individual organisms with unique traits (e.g., eye

color in humans, the toxins of Escherichia coli O157:H7). They also provide

the information needed to produce an incredible variety of proteins through an

indirect process that utilizes a transient intermediary molecule called messenger

RNA (mRNA). When the DNA code is to be translated, the two intertwined

strands of DNA are separated, and an RNA copy is made of one of the strands.

This RNA copy is then sent to the cell “machinery” that translates the code into

proteins. Proteins provide the structural components of cells and tissues, and

enzymes for essential biochemical reactions, but they can also act as toxins as

illustrated below.

PCR techniques are based on the principle that genetic elements such as

DNA or RNA, which are present at very low concentrations in water or other

materials, can be copied many times by specific enzymes (polymerases) and

subsequently detected via fairly standard biochemical methods. This

amplification process is fundamentally the same process used by living

creatures to duplicate their genes. A primer, or small stretch of DNA known to

match a portion of the target organism’s genome, is added to the concentrated

water (or other sample) to be tested. If it finds a match (i.e., if the organism’s

DNA is present), the polymerase chain reaction can take place, and the products

can be detected after amplification. It is typical for PCR to amplify the targeted

DNA by a factor of 10,000 to a millionfold. The products of the copy

(amplification) process are called amplicons.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 156

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE 6–2 Illustration of two complementary strands of DNA forming A-T

and G-C base pairs.

Protozoa

Several recent papers have described a variety of PCR-based protocols for

the detection of waterborne pathogens, including Cryptosporidium (Champliaud

et al., 1998; DiGiovanni et al., 1999; Kimbell et al., 1999; Kostrzynnska et al.,

1999; Sulaiman et al., 1999; Xiao et al., 1999a,b), microsporidia (Dowd et al.,

1998a,b), Cyclospora (Jinneman et al., 1996), and Giardia (Rochelle et al.,

1997a,b) in environmental samples. A comprehensive review of the use of PCR

techniques for the detection of protozoa has recently been published (Morgan

and Thompson, 1998).

The primers of PCR protocols for Cryptosporidium parasites are based on

undefined genomic sequences (Laxer et al., 1991; Morgan and Thompson,

1998) or specific genes (Johnson et al., 1995; Leng et al., 1996; Rochelle et al.,

1997a,b). Recent molecular studies have shown

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 157

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
that there are extensive genetic differences among different Cryptosporidium

species, as well as within C. parvum (Sulaiman et al., 2000). These inter- and

intraspecies differences have been used in the development of molecular

diagnostics tools for Cryptosporidium spp. and for genotype differentiation. For

example, Xiao et al. (1999a,b) evaluated several PCR techniques to determine

whether they could accurately detect C. parvum in environmental samples. The

authors concluded that two nested-PCR-restriction fragment length

polymorphisms (RFLPs) based on the small-subunit ribosomal RNA (rRNA)

(Xiao et al., 1999a,b) and dihydrofolate reductase genes (Gibbons et al., 1998)

were more sensitive than single-round PCR or PCR-RFLP protocols for

detection and Cryptosporidium species differentiation.

Using a slightly different approach, Kozwich et al. (2000) have capitalized

on the observation that a double-stranded RNA molecule, which is apparently

found only in C. parvum but not in other Cryptosporidium species (Khramtsov

et al., 2000); can be used as a “signature” sequence that is specific for this

organism. Using a novel solid-phase capture material and PCR, they were able

to detect levels of oocysts as low as one per liter in environmental samples. This

technology provides a simple reading that looks very much like the

commercially available pregnancy test.

For the water industry to make accurate human health risk assessments for

C. parvum, it is essential to have methods to detect viable, infectious oocysts in

water samples. DiGiovanni et al. (1999) described a new strategy for the

detection of infectious C. parvum oocysts in water samples, which combines

immunomagnetic separation (IMS) techniques for recovery of oocysts with in

vitro cell culturing and PCR. This method, by its very nature, requires that the

oocysts be viable if cell culture and subsequent PCR are to detect anything.

Assays that use dyes appear to overestimate the viability of oocysts after

disinfection with ozone, as judged by mouse infectivity studies (Bukhari et al.,

2000).

Not all of the molecular methods of interest are based on PCR techniques.

A fluorescent in situ hybridization (FISH) technique has been developed

recently that shows considerable promise as an indicator of C. parvum oocyst

viability (Vesey et al., 1998). The basic premise is that a viable oocyst, but not a

dead one, has genetic elements that can be found with a colored tag that is

visible when examined under a microscope. Technically, a fluorescent DNA

probe is targeted to the 18S rRNA of C. parvum on the premise that 18S rRNA

is usually present in viable organisms and is degraded by cellular ribonucleases

(RNases) in dead or dying cells. Another proposed target for this kind of simple

visual confirmation

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 158

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
of viability is β-tubulin mRNA (Widmer et al., 1999). While existing FISH

techniques are limited to measuring the viability of C. parvum oocysts and not

their infectivity (Neuman et al., 2000), as our understanding of the

Cryptosporidium genome becomes more sophisticated, the latter may become

possible.

Organisms other than Cryptosporidium have also been investigated with

PCR methods. Giardia-specific primers used in published reports amplify a 183-

bp product from the small subunit rRNA gene (Weiss et al., 1992); 218- and

171-bp amplicons from a giardin gene (Mahbubani et al., 1992); and a 163-bp

product from a heat shock protein gene (Abbaszadegan et al., 1993). Rochelle et

al. (1997a) reported a PCR assay based on primers that are specific for G.

lamblia in water samples and that amplify a 218-bp product. A PCR-based test

to detect Cyclospora has been developed by Jinneman et al. (1996). This

method amplifies a region of the Cyclospora 18S ribosomal RNA gene.

Specific nucleotide differences in the amplified segment can be used to

differentiate amplicons of two closely related genus of coccidian parasites

(Cyclospora sp. and Eimeria sp.) by digestion with the restriction endonuclease

Mnl I. Dowd et al. (1999b) assessed two methods for isolating microsporidia

DNA from water for use in PCR amplification of microsporidia target

sequences. Both of the DNA isolation methods when combined with PCR

showed the ability to detect less than 10 spores of human pathogenic

microsporidia in water.

Capitalizing on these advances, Orlandi and Lampel (2000) of the U.S.

Food and Drug Administration have published an extraction-free, filter-based

PCR template preparation technique for the detection of Cryptosporidium,

Cyclospora, and pathogenic microsporidia. This methodology does not require

DNA extraction; is rapid, efficient, and reproducible; and can be used in

multiplex PCR applications designed to detect multiple parasitic protozoa.

Viruses

The most frequently identified viruses in drinking water and/or associated

with waterborne outbreaks are hepatitis A, Norwalk virus and other

caliciviruses, coxsackievirus, rotavirus, and echovirus (Jaykus, 1997). The

cultivatable enteroviruses, which include poliovirus, echovirus, and

coxsackievirus, make up only a small percentage of the viruses present in

wastewater. The standard culture method is to inoculate mammalian cells with

these viruses or potentially contaminated water

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 159

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
and watch for damage to the cells (a “cytopathic effect”). Unfortunately, this

method has been shown to be insensitive for many viruses, which replicate but

do not destroy cells. Thus, PCR is now being utilized in cell culture to detect

noncytopathic viruses, and it has been used to screen for viruses in groundwater

(Abbaszadegan et al., 1999). In addition, new viruses (e.g., caliciviruses) are

constantly being identified that are nonculturable causes of gastroenteritis.

These human enteric viruses will have to be investigated in water using PCR

methods or their equivalent.

Hepatitis E virus (HEV) is the most common cause of acute hepatitis in

humans in many parts of the world, especially Asia, and is also found in wild

and domestic animals. Although mortality rates for this infection in humans are

generally low (<1 percent) as many as 25 percent of pregnant women who

acquire this infection will die (Aggarwal and Krawczynski, 2000). Waterborne

epidemics as well as person-to-person spread of this virus have been reported.

Recent genetic sequencing studies have found that human and pig HEV are very

similar genetically in the United States as well as in Nepal (Meng et al., 1997,

1998; Tsarev et al., 1999). Thus, zoonotic transmission seems very possible

(Smith, 2001). Although the cultivation of HEV has recently been reported

(Smith, 2001), reverse transcriptase PCR is the method of choice in water and

has been used successfully to detect HEV RNA in sewage (Jothikumar et al.,

1993; Pina et al., 1998).

The Norwalk-like caliciviruses (NLVs) are noncultivatable enteric viruses

(also known as small round structured viruses) that have been reported to cause

numerous waterborne outbreaks (Jaykus, 1997; Kaplan et al., 1982; Schaub and

Oshiro, 2000). A diverse group of RNA viruses, they are a common cause of

gastroenteritis, with diarrhea and or vomiting lasting approximately two days.

These viruses may be quite prevalent in the environment and have been

reported in sewage at 107 RNA-containing particles per liter using PCR

techniques (Lodder et al., 1999).

Bacteria

Because of their relative ease in manipulation in the laboratory, rapid

growth, and sophistication of our understanding of bacterial genomes, most of

the published literature dealing with the molecular characterization of

pathogens is focused on cultivable bacteria. Indeed, the insights developed in

this particular field have led to the whole-organism genomic sequencing

projects discussed earlier in this chapter.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 160

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
However, it is now apparent that even bacteria of very major public health

concern are not always culturable. This important issue is also discussed at

length later in this chapter under virulence factor “persistence.” One of the best

paradigms in this regard is the agent of cholera, Vibrio cholerae. This

organism’s favored natural environment is estuarine areas. It can enter a viable

but not culturable dormant state wherein it has greatly reduced needs for

nutrients and oxygen (see reviews by Colwell and Huq, 1994; Sanchez and

Taylor, 1997). The ingestion of these nonculturable forms by humans is

believed to allow for reversion to the highly pathogenic form, leading to cholera

disease and potential outbreaks. These forms can be detected via PCR methods,

as well as epifluorescent microscopy and acridine orange staining. Of note, it

has been shown directly that PCR methods can detect the presence of cholera

toxin genes even when the organisms are in a viable but nonculturable state

(Colwell and Huq, 1994). It is not generally appreciated that a number of other

highly pathogenic microorganisms such as Campylobacter jejuni, Salmonella

enteritidis, Escherichia coli, Helicobacter, and Legionella have the same ability

to enter into nonculturable form. These examples help support the conclusion of

the Biotechnology Research Working Group—a subcommittee of the National

Science and Technology Council (BRWG, 2000) —and this committee, that

methods other than culture must be used to fully evaluate microbial

contamination of drinking water.

A recent example of the successful application of PCR methods and the

use of genetics for waterborne pathogens was reported by Kingombe et al.

(1999) for the bacteria Aeromonas. Aeromonas hydrophila is included on the

1998 CCL (EPA, 1998a) and as previously noted is the only CCL

microorganism on the UCMR (EPA, 1999f,g) that will be monitored in the first

cycle of the UCMR. Yet the methodology proposed to evaluate and identify this

pathogen in water is based on a cultivation technique (Havelaar et al., 1987) and

will yield little information on the virulence or the pathogenic nature of the

isolates (EPA, 1999f). Aeromonas spp. are ubiquitous in nature and only

recently have the bacteria been associated with human illness. A large number

of virulence factors for this microorganism have been described in the literature.

The similarity between the cytolytic enterotoxin gene of A. hydrophila and the

aerolysin genes of Aeromonas spp. was recently used to characterize the

distribution of virulence in water (Kingombe et al., 1999), with 58 percent of

the isolates associated with potential health threats. The particularly

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 161

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
clever aspect of this work by Kingombe et al. (1999) is their use of molecular

techniques to detect virulence factors in a variety of Aeromonas species that are

of concern to humans.

There are more than 100 serotypes of Escherichia coli that are capable of

producing Shiga toxins (STXs) (Nataro and Kaper, 1998). The genes encoding

for STXs are located on bacteriophages and thus, may be spread from one

serotype to another. Law (2000) reports that the pathogenicity of any given E.

coli is likely related to a number of virulence factors in addition to the STX

gene. This finding supports the premise that it may be possible to identify the

potential for emerging types of pathogenic bacteria by understanding the array

of virulence genes that are present in environmental waters.

Gene Microarrays and Genomics

Gene chip technology (microarrays), has been used in research laboratories

since the late 1990s to study gene expression and is the subject of recent and

intense scientific interest. DNA microarrays are immobilized pieces of singlestranded manufactured DNA, typically spotted onto glass or nylon substrates,

that are used to capture key genetic targets.

The premise is that the DNA is spotted using an arrayer device that places

it on the slide in a known position. The arrayer device can be an expensive

robotic device or a simple ink-jet spotter (e.g., see “How to Build Your Own

Microarrayer” at http://cmgm.stanford.edu/pbrown/mguide). The DNA spotted

onto the chip is designed to bind a sequence of DNA that may or may not be in

the sample to be tested. Currently, most studies using this technology are

examining the entire spectrum of genes of an organism (its genome) to see how

the entire set of genes varies under different metabolic conditions or when

stressed by a malignancy or other circumstance (e.g., Aach et al., 2000; DeRisi

and Iyer, 1999). However, that need not be the only way in which this

technology is used.

For example, a chip could catch several thousand bits of DNA from

pathogenic microorganisms that are, or may be, waterborne. The chip would be

incubated in a water sample that had been treated to liberate the DNA from any

microorganisms. If the pathogenic microorganism of concern was present in the

sample, its DNA would bind to the bit of DNA that matched its own on the chip

(i.e., a complementary strand). This bound DNA could then be amplified using

PCR and subsequently

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 162

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
labeled (e.g., with fluorescent molecules) and detected using a variety of means

such as spectroscopy. Alternatively, DNA spotted onto the slide could act as a

“molecular beacon,” or a piece of DNA that becomes intensely fluorescent once

the target DNA binds to it (Tan et al., 2000). This once conceptual method has

already been used clinically to identify specific bacteria found in the

bloodstream of hospital patients (Anthony et al., 2000).

By 1998, microarrays were manufactured to assay 500 to 5,000 genes

(Marshall and Hodgson, 1998; Ramsay, 1998), and chips that can assay up to

100,000 genes are predicted to be available in the near future (Lander, 1999).

Although the experimental power of such arrays is considerable, current costs

for complete systems range from $25,000 to $135,000. Commercially available

systems are currently being produced for what is known as “gene discovery”

(i.e., rapid identification of targets in infectious disease).

The committee anticipates that in a very short period of time, microarrays

could be developed that are labeled with all of the genes for a variety of

virulence factors identified within enteric bacteria, pathogenic viruses,

opportunistic protozoa, and other (waterborne) microorganisms. Examples of

some of these are provided in the next section of this chapter. These gene chips

could be used to assay environmental and drinking water samples for the

presence of genetic virulence factors of concern. If such virulence factors were

present, the sample could be assayed further if needed to better identify the

microorganism. Thus, the commercial and public health implications are

enormous.

VIRULENCE AND POTENCY RESPONSE—HEALTH

ASPECTS

Introduction

Historically, it has been well recognized that the virulence of a microbial

pathogen can be correlated with some observable feature. In this section,

several examples of the current state of knowledge concerning the molecular

basis for variation in virulence, and what is known about some of the

underlying mechanisms of virulence itself, are discussed. This section is

intended to give the reader an overview of some of the microbial virulence

factors that have already been identified and could act as virulence factors in the

future development of a VFAR.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 163

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Viral Examples

Influenza—Mutation is Associated with Virulence

Influenza can be considered as a prototypic emerging disease. Although it

is not known to be waterborne, it is discussed here to support the contention that

specific virulence attributes can be mapped to an important and well-understood

pathogen that is widely recognized by the public. Influenza virus is zoonotic

(i.e., it infects and is maintained in other animals, chiefly birds and swine) and

is capable of mutating so frequently that immunity to one strain will not be

protective against another strain. It can also result in an innocuous infection or a

lethal one. In 1918, influenza was responsible for a worldwide pandemic that

killed more than 20 million people (Webster et al., 1992). More recently, a wellpublicized outbreak in Hong Kong in 1997 resulted in the deaths of 6 of 18

people who were confirmed to have been infected with a new serotype of

influenza (H5N1) (Snacken et al., 1999). For influenza, it became clear that the

outer surface proteins of the virus are closely linked to the degree of disease that

humans suffer.

Serotypes of influenza are classified by the neuraminidase (N) and

hemagglutinin (H) protein molecules on the surface of the virus. These are

encoded by the RNA-based genome of the virus. Several isolates of influenza

can infect the same animal at the same time, leading to gene reassortment

events in which the progeny virus are a mixture of the two parental strains.

Each of the major families of the neuraminidase and the hemagglutinin

molecules on the surface of the virus has a specific subtype name, such as N1,

N2, H1, H2, and so forth. Every year, a decision is made by U.S. public health

authorities regarding which subtypes to concentrate on for vaccine production

based upon infection information from elsewhere in the world. Once a decision

is made to manufacture a specific vaccine using specific serotypes, the vaccine

must be grown, inactivated, and packaged for delivery. In the case of the Hong

Kong outbreak, the new isolate was so virulent that it killed the chickens and

chicken eggs that are typically used to grow influenza virus for vaccine

production (Snacken et al., 1999). Thus, the Hong Kong isolate was so virulent

that to protect the human population adequately would have been extremely

difficult. Fortunately, its high virulence was apparently not matched by high

transmissibility, and it did not spread beyond Hong Kong.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 164

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Influenza thus represents a virus whose surface molecules correlate with

virulence, but researchers do not yet know why this is the case. Nonetheless,

genetic recognition of the molecules associated with virulence can serve as an

important public health tool.

Hepatitis E—An Emerging Waterborne Infection in the United States

Hepatitis E virus is the third leading cause of hepatitis, after hepatitis A

and B. It occurs worldwide, but rarely in developed countries such as the United

States (Labrique et al., 1999). As discussed earlier in this chapter, HEV is

predominantly a waterborne disease since direct transmission from person-toperson is uncommon (~2 percent of cases; Labrique et al., 1999). Disturbingly,

it now appears that transmission is occurring in the United States. Tsang and

colleagues (2000) reported a case of HEV in California. A man, who had not

traveled outside the United States for more than 10 years, drank water from a

well and a lake one month before becoming ill. In El Paso, Texas, 0.4 percent of

pregnant women (the group most at risk of death during acute infection) were

reported to have had serological evidence of prior exposure to this agent

(Redlinger et al., 1998). In Iowa, 4.9 percent of patients with non-A, non-B

hepatitis were seropositive for HEV (Karetnyi et al., 1999). In other developed

countries such as the United Kingdom, it has also become apparent that HEV is

a cause of acute hepatitis and is underdiagnosed (McCrudden et al., 2000).

Since HEV is principally a waterborne disease elsewhere in the world, it is

appropriate to be concerned about waterborne transmission in the United States.

Two people in the United States have been diagnosed as having HEV with

strains that are genetically different from the other HEVs that occur in less

developed countries (Kwo et al., 1997; Meng et al., 1997, 1998b). However,

these strains were nearly identical genetically to the recently discovered swine

HEV, which is found throughout the United States in swine populations. Other

mammals can also be infected with HEV in the United States. In a recent

nationwide survey by the Centers for Diseases Control and Prevention, 806

rodents (26 species in 15 genera) were tested and 60 percent of rats were

seropositive for HEV (Favorov et al., 2000). Tying these data to information

about transmission of HEV in the United States is the fact that actual clinical

differences have been reported between HEV strains within a genotype

(Labrique et al., 1999; Ticehurst, 1999; Tsarev et al., 1999).

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 165

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Thus, it is probable that modern molecular surveillance of U.S. drinking

water could detect HEV of animal and potentially human origin. Furthermore,

analysis of strains and genotypes in the detected HEV would probably lead to

further information regarding the potential for this pathogen to infect humans

who ingest such water. Moreover, tracking different genotypes could assist in

establishing dispersion patterns of HEV into the aquatic environment.

Poliovirus and Other Enteroviruses

Enteroviruses account for 10 million to 15 million infections per year in

the United States (Sawyer, 1999) and are easily detected in surface waters

(Melnick and Gerba, 1980). They are spread by waterborne transmission, direct

person-to-person contact, and common-source exposures such as swimming

pools. Paralytic poliomyelitis is often the dreaded consequence of infection by a

common enterovirus, poliovirus. This enteric virus has historically been

transmitted both in waterborne epidemics and through person-to-person contact.

The majority of children infected with polio have a simple undifferentiated

febrile illness or no symptoms, while others will develop aseptic meningitis or

paralysis. Despite virtual eradication of this endemic disease in the western

hemisphere through universal vaccination, it unfortunately remains common in

Africa and parts of Asia. Since 1980, all cases of poliomyelitis in the United

States have actually been caused by vaccine strains of polio, which is

exceedingly rare (Strebel et al., 1992). Any region with wild-type poliovirus can

act as a reservoir for reintroducing virulent poliovirus to areas that no longer

have endemic transmission problems, and this has happened frequently over the

past three decades (Kubli et al., 1987). Thus, continued surveillance as well as

vaccination for this virus is clearly warranted throughout the United States.

Three serotypes of poliovirus exist (Bodian et al., 1949). They share some

antigens but are characterized by marked intertypic differences (Melnick,

1996). Protection against this virus in conferred when a person has antibodies to

the three main structural proteins VP1, VP2, and VP3 that make up the viral

surface of all three serotypes. A fourth protein, VP4, is internal. Vaccination

with one serotype does not, however, confer adequate protection against the

other serotypes of polio (Melnick, 1955). Most of the epitopes, or sites that are

recognized by antibodies, lie on VP1. These VP proteins vary markedly

between serotypes and even within a given serotype. The tendency for

poliovirus to infect the

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 166

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
nervous system (and not just the intestine) is called neurotropism. Although all

three serotypes of poliovirus can infect the nervous system, some isolates are

more or less neurotropic. Indeed, the original Sabin (live, oral) vaccine was

developed and used because it lacked neurotropism (Sabin, 1985). Specific

mutations that are associated with the lack of neurotropism have been identified

(Mento et al., 1993; Ren et al., 1991).

The discovery of enteroviruses was closely linked to the poliovirus control

effort. Animal and tissue culture virus isolation studies, looking for poliovirus

isolates, often revealed other viruses of the poliovirus family, or enteroviruses

(Pallansch and Anderson, 1998). These include other groupings, such as the

coxsackievirus A, coxsackievirus B, and echovirus groups. Since it became

clear that all of these viral agents actually belonged to one family, all new

isolates have simply been classified as enteroviruses and numbered

sequentially. These agents cause diseases marked by rash (hand-foot-and-mouth

disease, herpangina); cardiac disease (myocarditis and pericarditis, which can

be fatal); respiratory tract infections; central nervous system disease including

paralysis identical to that of poliovirus; and a variety of other diseases.

Epidemiologic and observational studies have suggested that the development

of insulin-requiring diabetes mellitus is related to infection with enteroviruses

(e.g., Roivainen et al., 1998). The entire genome of any given enterovirus,

including polioviruses, is encoded by a single strand of RNA that is about 7,500

nucleotides long, and the entire genome of many enteroviruses is already

known. Moreover, the structure and physical properties of enteroviruses are

nearly identical to those of polioviruses (Pallansch and Anderson, 1998). Thus,

the very major variances in virulence and tissue tropism of all of the

enteroviruses are encoded in this one relatively short molecule.

At this time, the mere detection in the United States of a wild-type

poliovirus in a drinking water supply would be of very major public health

importance because endemic transmission is believed to have ended in this

hemisphere. Similarly, the monitoring of public drinking waters for the

presence of enteroviruses is likely to lead to important information regarding

the causal role of these viruses in a number of chronic diseases such as diabetes

mellitus. While traditional cell culture techniques required the (prohibitively

expensive) use of at least four cell types to screen for all enteroviruses (Dagan

and Menegus, 1986), the committee notes that powerful PCR methods have

recently been developed to detect enteroviruses in surface waters collected

under the Information Collection Rule (Chapron et al., 2000). This

methodology has

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 167

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
been developed at the same time that similar PCR techniques have been

evaluated and found to be cost-effective for the rapid diagnosis of enterovirus

infections in humans (Nigrovic and Chiang, 2000). Thus, the use of VFAR

molecular methods for monitoring enterovirus infections would (1) take

advantage of the immense knowledge base about virulence characteristics that

exists at the viral level and (2) tie into new clinical technologies that could be

used to confirm actual human disease characteristics with specific virulence

attributes.

Bacterial Examples

Although the previous discussion may have suggested that the knowledge

base for viral pathogens is extensive, the most robust knowledge about

pathogens actually lies in the realm of bacterial pathogens. This set of

examples, like the viral ones, begins with a pathogen that is generally

recognized by all members of the public—streptococcal infection.

Streptococcal Infections

Group A streptococci (Streptococcus pyogenes) are the bacteria that cause

“strep throat,” scarlet fever, and rheumatic fever, as well as the emerging “flesheating” syndromes with rapidly spreading infection and loss of limb and life

(necrotizing fasciitis). They have very well characterized exterior proteins that

are recognized as distinct serotypes (Lancefield, 1928).2 These serotypes are

determined by exterior cell wall M, T, and R antigens, which are glycoproteins

(proteins with sugars attached) encoded by the bacterial genome (Stollerman,

1998). The M protein, a major surface antigen, is the predominant virulence

attribute that predicts pathogenicity. It acts to prevent ingestion of the bacteria

by human phagocytic cells.

Acute glomerulonephritis, or renal failure, frequently follows a group A

streptococcal infection, and a very limited number of M and T serotypes are

associated with this form of kidney disease. Similarly, only specific serotypes

commonly cause rheumatic fever. Thus, there is an excellent correlation

between the presence of specific markers of

2 “Serotype” here refers to the common laboratory method of distinguishing between

strains of streptococci by using serological testing with antibodies.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 168

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
virulence—the M and T antigens—and actual adverse human outcomes.

Rheumatic heart disease was once one of the leading causes of death in the

United States, and it remains a major cause of morbidity and death in many

developing countries (Stollerman, 1998).

Streptococci also produce a very large number of toxins. Some of these

cause the bright red rash of scarlet fever, whereas others destroy and literally

dissolve human tissues. One set of these toxins causes toxic shock syndrome

and belongs to a family of toxins that is shared with an otherwise unrelated

bacterium Staphylococcus aureus. These pyrogenic toxins are relatively small

(molecular weight 20,000 to 30,000) and are similar on a genetic basis (Bohach

et al., 1989). They also provide an excellent example of how a virulence

attribute in one organism predicts virulence in an organism of a different genus

and species. Thus, there are well-known virulence attributes that predict

pathogenicity within species (M proteins) and across species (toxic shock

toxins).

Enteric Bacteria

Most of the bacteria that cause diarrhea or dysentery (bloody diarrhea) do

so after a set of interactions with the human host. These interactions include (1)

ingestion by the host; (2) survival after passage through the acidic environment

of the stomach; (3) attachment to the lining of the host’s intestine (epithelium);

and (4) production of compounds that induce the host to secrete fluid that leads

to diarrhea and/or to envelop the invading bacteria so it can enter the host cell.

Bacteria are far more complex than typical viruses and have the capacity to

devote energy and part of their genome to accomplish each of the above tasks

successfully. Moreover, bacteria have the ability to share the genes for these

tasks with each other.

This sharing of toxin genes and other virulence factors means that bacteria

detected within the United States may have their native representatives that are

just as harmful. For example, Shigella bacteria (a cause of dysentery) are now

rarely of major concern in the United States because hygienic standards are

generally high. Shigella dysenteriae produces a highly lethal toxin called Shiga

toxin (Acheson, 1998; Acheson et al., 1991; Conradi, 1903; Keusch, 1998;

Keusch et al., 1972). Other Shigella species (e.g., S. sonnei, flexneri, boydii) are

unlikely to make this toxin in similar quantities (Keusch, 1998). STX is

composed of one A unit and five B subunits. The five B subunits first attach to

a host cell, the toxin penetrates; and then the A subunit kills the cell. Although

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 169

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
originally described from the bacterium Shigella dysenteriae type 1, an

essentially identical set of toxins is made by Escherichia coli O157:H7, which

is why this and other E. coli strains that make the toxin are of such public health

concern.

Escherichia coli O157:H7 is frequently carried in cattle, has been

implicated in many raw beef or hamburger-related epidemics, and has been

implicated as the causative lethal bacterium involved in a number of waterborne

disease outbreaks (Anonymous, 1999; Swerdlow et al., 1992). Haas et al.

(2000) estimated that the median infectious dose is 5.96×105 organisms in

drinking water and that ingestion of only 4,000 organisms would result in a 1

percent attack rate. Nevertheless, average ingested doses below this level can

still pose a significant public health threat, particularly if exposure of large

population occurs. For example, Tuttle et al. (1999) found that the median

“most probable” number of organisms in contaminated hamburger patties

involved in a recent widespread outbreak in Canada was only 67.5.

A very closely related toxin called Shiga toxin 2 (STX2) is also produced

by E. coli and has three major known biological variants (WHO, 1998). The

majority of STX-type genes are actually present on a bacteriophage that infects

the E. coli bacteria, fostering transfer between bacteria. It is these toxins that are

thought to cause the bloody diarrhea and frequent death of people who have

shigellosis or who are infected with E. coli strains that produce these toxins.

The mere presence of these genes and the ability to make this toxin may not,

however, always be sufficient to cause disease. The bacteria often attach to the

human host intestine after having entered the digestive tract, and this requires a

set of genes that facilitate this type of interactions.

The attachment of these bacteria to the intestinal wall is through a

characteristic attaching and effacing (A/E) cytopathic lesion. The ability to

attach and efface is encoded by 41 genes in a “pathogenicity island” or “locus

for enterocyte effacement” (LEE). This island or locus encodes the adhesin

called intimin on the eaeA gene, as well as other factors that are needed for

secretion of these molecules into the space between the bacterium and the host

cell. This group of genes can be shared among all of the bacteria that adhere to

the intestine via this mechanism (Acheson, 1998; Goosney et al., 1998; WHO,

1998).

Some enteric bacteria invade the host cell and evade the immune response,

essentially “hiding” within the host. This is a very dangerous property of such

bacteria, which is very strongly correlated with virulence. These bacteria

include Shigella, Salmonella, Yersinia, Listeria, and others. Some of these

enteric bacteria literally blow a hole in the

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 170

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
host cell as they move into an adjacent cell and accomplish this by causing host

proteins to jell behind them and force them forward like a jet-propelled object.

These genes are shared between bacteria that are as unrelated as Shigella

(Goosney et al., 1998) and Listeria (Chakraborty et al., 1995). The genes that

encode for such destructive characteristics are known and are highly associated

with virulence. They also represent an example of how a VFAR relationship,

which ties specific genes that encode for virulence factors, can correspond to

pathogenic effects in humans.

Summary

It is beyond the scope of this chapter to comprehensively review the very

extensive knowledge base that already exists regarding microbial genes that are

associated with virulence. However, several examples have been discussed and

serve to help make the following points:

• Because of their public health risks, for more than a century scientific

interest has existed in pathogens and their associated virulence factors, and

the scientific foundation for these measures of virulence is appreciable.

• Virulence is often clearly and easily linked to known genetic elements.

• The genetic elements associated with virulence vary somewhat among

isolates of specific pathogens, and this variation has been linked directly to

the genetic variation within many species of bacteria or viruses (e.g.,

polioviruses, influenza, Shigella toxins).

• These genetic elements are frequently shared among waterborne pathogens

having a similar ecological niche that warrants public health concern (e.g.,

Salmonella, Shigella, Vibrio, Escherichia bacteria).

• The ease of identification of these genetic elements, their sequencing, and

comparison has increased dramatically in the past decade due to advances

in molecular biology and bioinformatics.

PERSISTENCE RESPONSES

When pathogens are released into the aquatic environment they may die,

multiply, or enter a dormant state. Each of these has implications for

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 171

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
subsequent human exposure and health. The factors responsible for pathogen

decay are reviewed below. It is important to note at the outset that the

assessment of pathogen survival is dependent on the assay used for analysis.

This relationship has been particularly well documented for bacteria (Roszak

and Colwell, 1987). Further discussion of this issue is provided later in the

chapter. It is one of the committee’s central assertions that the assessment of

persistence (survival) in the environment using molecular techniques may be

superior to some of the older methods.

Mechanisms Responsible for Decay

The factors that can cause removal of pathogens from the aquatic

environment can be grouped broadly into abiotic and biotic factors, based on

the mediation of other organisms in the process. In any environment, overall

pathogen loss rates reflect the combination of these factors (Auer and Niehaus,

1993; Canale et al., 1993; Chamberlin and Mitchell, 1978).

Abiotic Factors

A number of abiotic factors may be responsible for microbial loss in the

aquatic environment. Perhaps the most straightforward is sedimentation.

Although environmental pathogens have quite low settling velocities by virtue

of their size and density, many organisms can become attached to particles in

the water column and increase their effective settling velocity to that of the

aggregate. This process has been demonstrated clearly for Cryptosporidium

(Medema et al., 1998), and similar phenomena are likely to occur with other

microorganisms.3

Although, the physical removal of microorganisms via sedimentation

represents a loss process from the water column, it does not result in total

elimination of the pathogens from the aquatic environment. In fact, there is

strong circumstantial evidence that the transport of pathogens into sediments

may create a reservoir for recontamination of the water column. Furthermore,

microorganisms in sediments may have lower net inactivation rates than

microorganisms in the water column (Burton et

3 For example, there is information that bacteria stick to solid surfaces in the milieu of

filtration in aquifer-like conditions (Bolster et al., 1998).

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 172

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
al., 1987; Davies et al., 1995; Laliberte and Grimes, 1982; Matson et al., 1978;

Palmer, 1988; Smith et al., 1978).

The effect of visible light on increasing decay rates of pathogens (and

indicator organisms) in water is well documented (Chamberlin and Mitchell,

1978; Davies-Colley et al., 1994; Mancini, 1978; Muela et al., 2000). It is

known that the penetration of sunlight into natural waters can produce a variety

of photochemical oxidants, including hydroxyl radicals and hydrogen peroxide.

Thus, the promotion of pathogen decay by light may involve direct, as well as

indirect (oxidant-mediated), mechanisms (Arana et al., 1992).

Biotic Factors

Interactions between pathogens and other organisms in water may result in

loss of viability of the pathogens. Among the more commonly documented

processes is the predation of bacteria by indigenous protozoan organisms

(Davies et al., 1995; Enzinger and Cooper, 1976; Sibille et al., 1998). The

obligate parasitic bacteria Bdellovibrio may also serve to diminish pathogenic

(and indicator) bacterial levels (Fry and Staples, 1974). Predation of bacteria by

bacterioviruses (phage) may also occur in natural waters (Bergh et al., 1989).

Although the evidence for this is not well documented, it is known that phage

exist for many pathogenic bacteria.

However, a recent review by Wommack and Colwell (2000) focusing

primarily on viral interactions with indigenous bacterioplankton (most of which

are nonpathogenic), stated that “less than 20% of bacterioplankton and

phytoplankton [in natural waters] is attributable to viral infection.” They

concluded that mortality loss from infection tends to increase as the density of

the host bacteria increases. Thus, it would be anticipated that the persistence of

pathogenic bacteria (or other potential hosts, such as algae or protozoa) would

be impacted minimally by the potential for viral infection.

Mechanisms Affording Protection to Microorganisms

There are several potential mechanisms that protect microorganisms

against adverse environmental conditions. Foremost among these is association

with solids that confer protection from die-off (Gerba et al., 1978). The

mechanism whereby this protection occurs has not been

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 173

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
well-characterized but may result from protection against biotic antagonists or

from transport of inhibitory substances to the solid-associated microbe. Some

microorganisms are able to enter dormant stages (e.g., bacterial spores), which

affords a measure of protection against adverse environmental conditions. For

example, the relatively strong persistence of nonpathogenic Clostridium

perfringens in water has been well documented (Cabelli, 1977; Fujioka and

Shizumura, 1985).

The formation of cysts in protozoa such as Giardia (Adam, 1991) or

oocysts by Cryptosporidium (O’Donoghue, 1995) results in a life stage that is

able to survive for extended periods in the environment. Helminthic ova also

represent a similar dormant or protective stage, and Ascaris lumbricoides ova

have been documented to survive for years and even decades in the soil

(Bergstrom and Langeland, 1981; Buts, 1969; Kizeval’ter and Derevitskaia,

1968; Kransnonos, 1978).

In some cases, microorganisms may occupy habitats that offer unique

protection against environmental agents. For example, it is has been established

that intracellular association with aquatic protozoa may be important in

protecting Legionella pneumophila from adverse environmental conditions

(Barker et al., 1992; Fields et al., 1984). This association may also assist in the

resuscitation of “viable nonculturable” Legionella (see more below) (Steinert et

al., 1997). Other bacteria (and perhaps other nonbacterial pathogens) may be

harbored protectively in this manner. For example, King et al. (1988) reported

that the protozoan Tetrahymena could harbor and protect cells of Escherichia

coli, Citrobacter freundii, Enterobacter agglomerans, E. cloacae, Klebsiella

pneumoniae, K. oxytoca, Salmonella typhimurium, Yersinia entercolitica, 

Shigella sonnei, Legionella gormanii, and Campylobacter jejuni for at least

several hours. The relative importance of pathogen harboring by indigenous

protozoa versus diminution of pathogen levels through predation in aquatic

environments deserves further study. The committee contends that the

molecular methods discussed in this chapter are likely to prove useful in

assessing the viability of “harbored” pathogens.

Although the concept is controversial (Bogosian et al., 1996; Bogosian et

al., 1998; Kell et al., 1998), a number of researchers have indicated that bacteria

can enter a dormant stage, generally termed “viable nonculturable.” This was

first shown in studies on Vibrio cholerae, in which the viable nonculturable

state apparently plays an important role in the maintenance of the pathogen in

the water column (Brayton et al., 1987; Colwell, 1996; Ravel et al., 1995). (This

topic is discussed earlier in this chapter; however, the focus there is on the value

of PCR techniques

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 174

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
in detecting nonculturable bacteria rather than on persistence responses.)

In some cases, however, the pathogenicity of the putative viable

nonculturable organisms may be low, as reported by Caro et al. (1999) for

Salmonella typhimurium. In this dormant state, assessment of microbial levels

by plate counts may indicate little occurrence (unless resuscitation has been

triggered), while other assays such as total microscopic count or nucleic acid

assay may indicate higher levels. Table 6–2 summarizes some published reports

of the occurrence of viable nonculturable states in bacteria.

Range of Decay Rates

Regardless of the mechanism(s) promoting loss of viability of

microorganisms in aquatic systems, several researchers have reported the

observed rate of disappearance under different conditions as summarized below.

TABLE 6–2 Viable Nonculturable States in Bacteria

Microorganism References

Campylobacter jejuni Buswell et al., 1998

Rollins and Colwell, 1986

Coliforms McFeters et al., 1986

Escherichia coli O157:H7 Rigsbee et al., 1997

Klebsiella pneumoniae Byrd et al., 1991

Enterobacter aerogenes

Agrobacterium tumefaciens

Streptococcus faecalis

Micrococcus flavus

Salmonella enteriditis Chmielewski and Frank, 1995

Salmonella dysenteriae Islam et al., 1993

Enterococcus faecalis Lleo et al., 1998

Legionella pneumophila Steinert et al., 1997

Vibrio vulnificus Weichartand Kjelleberg, 1996

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 175

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Determination of Viability

For studies that measure decay rates in microorganisms, the particular

assays used for assessment of viability become important. In most cases, but

particularly for data obtained before the mid-1990s, the assessment of viability

was based on culturing methods such as agar plate growth of bacteria and mostprobable-number assays for bacteria or viruses (in the form of the TCID50

determination [i.e., tissue culture infectious dose, or dose required to infect 50

percent of the tissue culture in which a sample is inoculated]).

As noted previously, however, bacteria may form viable nonculturable

stages that by definition are not readily enumerated using culture techniques.

Therefore, reliance on culture techniques may incorrectly estimate the true

decay rates. In more recent years, some investigators have used molecular

genetic techniques such as PCR (Abbaszadegan et al., 1999; DiGiovanni et al.,

1999; Shieh et al., 1997; Sturbaum et al., 1998) to assess occurrence or decay of

pathogens in environmental systems. Although PCR and other molecular

methods may allow more efficient data collection, they may also overestimate

the occurrence or persistence of viable microorganisms (Deere et al., 1996;

Dupray et al., 1997). Thus, any reports of survival times (or occurrences) of

pathogens in water should be accompanied by a description of the methods used

to assess viability.

Data for Established Pathogens

As part of a mid-1980s reevaluation of the coliform standards for drinking

water, a comprehensive review of the decay of indicators and pathogens in

water was performed under the sponsorship of EPA. A summary of these decay

values is provided in Table 6–3. The original tabulated values of times required

for 50, 90, 99, or 99.9 percent reduction are indicated—this is preferable to

conversion to a single metric (e.g., half-life), since in many cases the underlying

data differed from ideal first-order decay. The information in this table reflects

microorganism survival under diverse conditions ranging from raw water (of

various sources) to finished drinking water (although in no circumstances was

there any disinfectant residual present).

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 176

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Data for More Recent Pathogens

There have been studies of additional microorganisms since the efforts

summarized below in Table 6–3. Although a comprehensive review of such

studies is beyond the objective of this report, a brief synopsis of findings for

some emerging pathogens is appropriate. DeRegnier et al. (1989) suspended

Giardia muris in river water and lakewater at ambient temperature to monitor

viability using propidium iodide and animal infectivity. As measured by

infectivity, cysts remained viable at least 40 days. It should be noted, however,

that the small number of test animals did not likely permit measurement of

inactivation beyond one log. The authors concluded

...G. muris cysts suspended in environmental water remained viable for 2 to 3

months, and their survival was enhanced by exposure to low water

temperature, despite the fact that the cysts were suspended in the fecal biomass

within the sample vial.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 177

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Robertson et al. (1992) used a differential dye inclusion assay to monitor

viability decay of two strains of Cryptosporidium parvum oocysts. One was

originally isolated from deer and cultured in sheep, and the other was a bovine

isolate. The organisms were held in membrane diffusion chambers in river

water under ambient conditions. Their results indicate that 1-log inactivation is

estimated to occur at 100 and 180 days for the two strains examined. More

recently, Jenkins et al. (1997) determined that the survival of oocysts in fecal

material (as measured by vital dyes) correlates well with the ability of the

oocysts to excyst. However there is continuing controversy about the suitability

of dye incorporation assays versus other techniques with respect to assessing

oocyst viability (Belosevic et al., 1997; Bukhari et al., 2000). As noted earlier in

this chapter, FISH techniques that use mRNA or other targets may be superior

to estimate microbial viability. Enriquez et al. (1995) reported that adenovirus

held for 60 days in dechlorinated tap water produced only a 2-log reduction at

23°C as measured through tissue culture assays.

Intrinsic Factors Influencing Decay

A key question is to what degree the persistence or decay of pathogens in

water can be predicted quantitatively and how this information could be used in

the construction of VFARs. Based on the preceding information, it is clear that

a wide range of variation exists in the removal rates of pathogens in aquatic

systems. However, beyond some broad generalizations, a fully quantitative

model that incorporates effects of adverse conditions on a range of pathogens

has eluded investigators. It is encouraging, however, that in some cases for

specific microorganisms, an overall predicted model can be developed (Auer

and Niehaus, 1993; Auer et al., 1998; Canale et al., 1993; Chamberlin and

Mitchell, 1978; Mancini, 1978).

Nonetheless, there are identifiable differences between microorganisms

that should allow for a semiquantitative assessment of environmental

persistence; these are

• ability to sorb suspended solids;4

• ability to form dormant stages, including viable nonculturable;

4 Assuming that sorption to suspended solids does not result in increased decay rates.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 178

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
• ability to survive and/or multiply within aquatic protozoa or other microbial

hosts;

• ability to survive freezing;

• ability to survive desiccation;

• ability to survive wastewater treatment and to reenter drinking water; and

• ability to survive in anaerobic sediment.

INTERPRETATION OF ISSUES

Data Information and Management Issues

Although the technology, methodology, and even the genetic databanks

exist, the application of a VFAR approach to assess waterborne pathogens

would require considerable effort and expenditure of resources by EPA in

conjunction with the Centers for Disease Control and Prevention, National

Institutes of Health, and other federal and state health organizations (NRC,

1999b). Such an interagency “Waterborne Microbial Genomics” (WMG)

project would also require extensive expertise in bioinformatics, molecular

microbiology, environmental microbiology, and infectious disease. Initially,

existing gene banks would have to be screened and evaluated for key targets.

For example, the National Center for Biotechnology Information jointly

established by the National Library of Medicine and the National Institutes of

Health maintains GenBank (www.ncbi.nlm.nih.gov). However, the available

sequence data are not error free, and greater quality control and quality

assurance would have to go into screening the genetic information. Of course,

new data on genetic sequences would have to be added to the WMG as they are

reported in the clinical literature. As a start, this would require the evaluation of

literature for references to microorganisms having the potential to be

waterborne, found in feces or urine, and naturally occurring in the water

environment.

Furthermore, protocols would have to be established and tested regarding

data entry, validation, and use in the development of microbial VFARs.

Background levels and determination of prevalence, persistence, and quantity of

key target genes would have to be gathered for analysis and interpretation of

health risk and/or exposure potential. Outbreaks and contamination events

would provide useful information to enhance the database. Once key parameters

had been established, the

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 179

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
possibility of screening hundreds of water samples for thousands of key

microbial hazards could be achieved through development of custom chip arrays.

The use of appropriate data and sophisticated data management would be

crucial to the development and validation of VFARs. Because VFARs have the

potential to be very powerful, they will require thorough validation and careful

use, with attention to the limits of their validity. Validation can proceed in a

variety of ways, such as the statistical measure of how well a VFAR fits into the

descriptor or response data, the use of sensitivity analysis to validate VFAR

data that are already available, using a VFAR with new data after it has been

established with older data, and comparing the predictions of one VFAR to

those of another. All of these will require the development of appropriate data

sets and data management tools. The committee also notes that the use of

prototype classification methods to help select PCCL contaminants for

inclusion on a CCL can obviously be applied to VFARs. That is, training sets of

descriptor and response variables could be developed and used in conjunction

with prototype classification methods to help derive VFARs. This again implies

that such training sets would have to be appropriate and robust.

The committee fully recognizes that just the initial establishment of such a

program (excluding maintenance and expansion) is likely to require at least a

five-year commitment and significant cooperation and expenditure of resources

by EPA and other participating organizations. However, the opportunities for

rapid identification of microbial hazards in water afforded by such a program

would greatly improve the ability of EPA to quickly and successfully protect

public health and improve water quality.

FEASIBILITY

For the VFAR concept to be ultimately adopted and used by EPA in its

drinking water program, it must be feasible. Several aspects of feasibility are

discussed below, including scientific validity and applicability; actual

technological feasibility; application of these technologies to studying disease

in humans (validation); the degree to which these methodologies are being

universally adopted within the scientific community; and the need for their

development and use to adhere to the principles of transparency, public

participation, and other sociopolitical considerations reviewed in Chapter 2. To

one extent or another, each of these elements

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 180

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
affects the ability of the VFAR concept to be developed, used, or validated.

Since these elements either are present or can reasonably be expected to be

available in the near future, the committee strongly concludes that the

development and use of VFARs is indeed feasible. Having carefully noted some

caveats and limitations in the preceding text, the committee remains

enthusiastic about the utility of developing and using VFARs in the protection

of the nation’s drinking water.

First, the underlying concept must be scientifically valid and robustly

applicable. As previously noted, the relationship between specific microbial

attributes and human disease has been known for more than a century. This

linkage has become increasingly documented and precise as our knowledge of

microorganisms and human disease has dramatically improved in the past few

decades. While the illustrative examples provided in this chapter are by no

means exhaustive, they certainly speak to the power of specific microbial

attributes to predict virulence in humans. It is not only possible, but in fact now

routine, to associate very specific human disease outcomes with the presence,

absence, or variability of specific microbial characteristics (or “descriptors” in

the language of SARs). To state that the concept is robustly applicable, the

committee means that it is neither limited nor narrow, but is in fact valid across

a very large number of microorganisms and remains valid when small

variations in a single organism are explored in great depth. The fact that this is

indeed the case, and is considered a paradigm of biomedical science, provides a

convincing demonstration of the validity and robustness of the concept. Thus,

the committee deems that the first necessary condition for feasibility exists.

For the VFAR concept to be useful, it must be able to extend a known

relationship between a virulence attribute and human disease to a situation in

which the attribute is found in a new or unexpected circumstance or in

microorganisms that have not heretofore been recognized as potentially

pathogenic. The profound scientific revolution associated with the unraveling of

DNA’s double helix speaks to this second aspect of feasibility—the disciplines

of bioinformatics, proteomics, and genomics. The ability to rapidly, and

completely, sequence the genome of entire organisms, and to use bioinformatic

techniques to compare gene sequences of different organisms, provides this

mechanism for comparison. The committee adds that the time and cost required

for sequencing a microorganism have both declined markedly in just the past

few years. Independent of any judgment made by this committee, a number of

biotechnology and pharmaceutical companies have chosen to aggressively

pursue opportunities that rely on comparative genomics, which the committee

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 181

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
regards as confirming its judgment of the adequacy, power, and affordability of

the methodology. The committee thus warrants that the necessary condition of

technological feasibility also exists.

Although the committee is unclear as to whether compelling logic

currently exists for the development of VFARs based on genomic techniques

that can be extended to proteomics, it fully acknowledges the potential for this

in the near future. The possibility for proteomics to also play a role in the

development of VFARs adds both depth and an additional dimension to ways in

which VFARs might operate in the future within EPA’s drinking water program.

A third element by which to judge feasibility is the likelihood that adverse

human outcomes (e.g., diseases) will continue to be discovered in association

with the action or presence of a microbial contaminant, microbial gene, or gene

product in the clinical setting. This clinical linkage between diseases and

specific microorganisms or their products has been a hallmark of medical

sciences for the past two centuries. There is no indication that the accelerating

pace of these medical discoveries is abating; one need only consider prions and

mad cow disease, Ebola virus, Escherichia coli O157:H7, Helicobacter pylori,

and nanobacteria to be reminded of important pathogens that were unknown to

science until a few decades ago. As discussed previously, the intent of the

VFAR is to characterize, categorize, and make scientific the potential linkage,

as outlined in Figure 6–3 below.

This external element represents both a validation of the linkage for those

organisms already known to cause disease and an element that will be helpful in

validating the discovery of emerging waterborne pathogens through the use of

VFARs. For “established” waterborne microoorganisms such as Vibrio

cholerae, Salmonella, and the pathogenic protozoa, this linkage was, and is,

made easily. That is, these microorganisms are already easily cultured or visible

in human specimens—no new technological or scientific advances are required

for them to be linked to human disease. In the case of emerging microorganisms

that are often unsuspected agents of disease, or are difficult to detect using

traditional methods of culture or microscopy, it is likely that the novel

molecular detection techniques discussed in this chapter (e.g., PCR, gene chips)

will continue to lead to new medical associations. Identification of some viruses

(e.g., herpes simplex virus) or specific microbial antigens through PCR

techniques has now become commercially viable and widespread in the medical

setting, replacing earlier methods. Many diagnostic kits are now available to

detect antigens shed by pathogenic bacteria and viruses in urine, blood, and

spinal fluid. Viruses that were once “too expensive”

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 182

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE 6–3 Linkage between microbial virulence factors and human disease.

to look for on a routine basis (e.g., rotavirus) are now tested routinely for

in the clinical microbiology laboratory using simple ELISA (enzyme-linked

immunosorbent assay) methods. Indeed, ELISA kits that recognize Giardia and

Cryptosporidium antigens are already available (e.g., Alexon-Trend’s

ProSpecT® assays). Thus, the committee can foresee the likelihood that the

same technologies needed for constructing VFARs will already be used in the

clinical setting to detect microorganisms or their products, further strengthening

the utility of the VFAR relationship.

A fourth element regarding feasibility is the likelihood that EPA’s

movement to adopt VFARs for use in its drinking water program will be

congruent with the direction that other government, private, and public agencies

are taking. In simplest terms, is such an effort likely to be adopted solely by

EPA, or is it likely to be a direction that other agencies will follow?

Resoundingly, all evidence points to a massive public and private investment in

genomics, bioinformatics, and proteomics, which are the key disciplines behind

developing and using VFARs. Furthermore,

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 183

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
the specific needs of EPA are likely to be recognized by others as being of

crucial importance to shared problems. For example, the “identification”

problem that exists for EPA—identifying potentially pathogenic microbial

water contaminants that are difficult or impossible to culture—has already been

recognized by other government agencies as a high-priority area for research

(BRWG, 2000). There is every reason to believe that the path EPA must follow

to develop VFARs will be similar to one already blazed by many other

agencies. Indeed, it can be argued persuasively that EPA will benefit very

substantially from the synergistic efforts of other agencies and independent

outside researchers as recommended in the committee’s second report (NRC,

1999a). In all likelihood, the resources of EPA will have to be directed

primarily to (1) focusing its own internal efforts and the attention of other

government agencies on waterborne pathogens and (2) integrating a tremendous

knowledge base (in large part developed outside the agency) with the purpose

of VFAR construction, validation, and use in EPA’s drinking water program.

Thus, adoption of the technologies behind VFARs by the wider scientific

community considerably improves the feasibility of any related EPA efforts to

develop and use VFARs. The high-priority issues identified in this report are

well recognized by other agencies that have similar needs but different

applications in mind.

Lastly, the committee’s (Chapter 2) recommendations that the process for

selecting drinking water contaminants for future CCLs be systematic,

scientifically sound, transparent, and involve broad public participation should

also be met in the development and use of VFARs.

CONCLUSIONS AND RECOMMENDATIONS

Despite the identification and discussion of some necessary caveats and

limitations, the committee concludes that the construction and eventual use of

VFARs in EPA’s drinking water program are feasible and merit careful

consideration. More specifically, the committee makes the following

recommendations:

• Establish a scientific VFAR Working Group on bioinformatics, genomics,

and proteomics, with a charge to study these disciplines on an ongoing

basis and periodically inform the agency as to how these disciplines can

affect the identification and selection of drinking water contaminants for

future regulatory, monitoring, and research activities. The committee

acknowledges the importance of several practical considerations

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 184

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
related to the formation of such a working group within EPA, including

how it should be administered and supported (e.g., logistically and

financially) or where it could be located. However, the committee did not

have sufficient time in its meetings to address these issues or make any

related recommendations.

• The findings of this report, and especially those of the Biotechnology

Research Working Group (BRWG, 2000) should be made available to the

VFAR Working Group at its inception. The committee views the activities

of such a working group as a continuing process in which developments in

the fields of bioinformatics, genomics, and proteomics can be assessed

rapidly and adopted for use in EPA’s drinking water program.

• The working group should be charged with the task of delineating specific

steps and related issues and time lines needed to take VFARs beyond the

conceptual framework of this report to actual development and

implementation by EPA. All such efforts should be made in open

cooperation with the public, stakeholders, and the scientific community

• With the assistance of the VFAR Working Group, EPA should identify and

fund pilot bioinformatic projects that use genomics and proteomics to gain

practical experience that can be applied to the development of VFARs

while it simultaneously dispatches the charges outlined in the two previous

recommendations.

• EPA should employ and work with scientific personnel trained in the fields

of bioinformatics, genomics, and proteomics to assist the agency in

focusing efforts on identifying and addressing emerging waterborne

microorganisms.

• EPA should participate fully in all ongoing and planned U.S. government

efforts in bioinformatics, genomics, and proteomics as potentially related to

the identification and selection of waterborne pathogens for regulatory

consideration.

VIRULENCE-FACTOR ACTIVITY RELATIONSHIPS 185

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
References

Aach, J., W.Rindone, and G.M.Church. 2000. Systematic management and analysis of yeast gene

expression data. Genome Research 10:431–445.

Abbaszadegan, M., M.S.Huber, C.P.Gerba, and I.L.Pepper. 1993. Detection of enteroviruses in

groundwater with the polymerase chain reaction. Applied and Environmental

Microbiology 59:1318–1324.

Abbaszadegan, M., P.Stewart, and M.LeChevallier. 1999. A strategy for detection of viruses in

groundwater by PCR. Applied and Environmental Microbiology 65(2):444–449.

Acheson, D.W. 1998. Nomenclature of enterotoxins [comment]. Lancet 351(9108):1003.

Acheson, D.W.K, A.Donohue-Rolfe, and G.T.Keusch. 1991. The family of Shiga and Shiga-like

toxins. Pp. 415–433 in Alouf, J.E, and J.H.Freer (eds.) Sourcebook of Bacterial Protein

Toxins. London: Academic Press.

Adam, R. 1991. The biology of Giardia spp. Microbiological Reviews 55(4):706–732.

Adam, R.D. 2000. The Giardia lamblia genome. International Journal for Parasitology 30:475–484.

Aggarwal, R., and K.Krawczynski. 2000. Hepatitis E: An overview and recent advances in clinical

and laboratory research. Journal of Gastroenterology and Hepatology 15(1):9–20.

Anderson, C.W. 1993. Recommending a scheme of reason: Political theory, policy science, and

democracy. Policy Sciences 26:215–227.

Anonymous. 1999. Outbreak of Escherichia coli O157:H7 and Campylobacter among attendees of

the Washington County Fair-New York, 1999. MMWR 48(36):803–805.

Anthony, R.M., T.J.Brown, and G.L.French. 2000. Rapid diagnosis of bacteremia by universal

amplification of 23 S ribosomal DNA followed by hybridization to an ologonucleotide

array. Journal of Clinical Microbiology 39:781–788.

Arana, I., A.Muela, J.Iriberri, L.Egea, and I.Barcina. 1992. Role of hydrogen peroxide in loss of

culturability mediated by visible light in Escherichia coli

REFERENCES 186

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
in a freshwater ecosystem. Applied and Environmental Microbiology 58(12):3903–3907.

Arneson, T., and E.Nord. 1999. The value of DALY life: Problems with ethics and validity of

disability adjusted life years. British Medical Journal 319:1423–1425.

ATSDR (Agency for Toxic Substances and Disease Registry). 1997. Toxicological Profile for

Tetrachloroethylene (Update). Atlanta, Ga.: U.S. Department of Health and Human

Services, U.S. Public Health Service.

Auer, M.T., and S.L.Niehaus. 1993. Modeling fecal coliform bacteria—I. Field and laboratory

determination of loss kinetics. Water Research 27(4): 693–701.

Auer, M.T., S.T.Bagley, D.A.Stern, and M.J.Babiera. 1998. A framework for modeling the fate and

transport of Giardia and Cryptosporidium in surface waters. Journal of Lake and Reservoir

Management 14(2–3):393–400.

AWWA (American Water Works Association). 1997. Workshop Report: Mission Definition for the

National Contaminant Occurrence Database (NCOD). Preliminary Draft Report. Prepared

by M.M.Frey, and J.S. Rosen. Denver, Colo.: AWWA.

AWWARF (American Water Works Association Research Foundation). 2000. Chapter 1 and 5 In

Research Needs Report: Report from Expert Workshop on Drinking Water Research.

Denver, Colo.: AWWARF.

Bailey, D., and D.Thompson. 1990. How to develop neural-network application. AI Expert 5(6):38–

47.

Balbus, J, R.Parkin, and M.Embrey. 2000. Susceptibility in microbial risk assessment: Definitions

and research needs. Environmental Health Perspectives 108(9):901–905.

Ballantine, L.G., and J.E.McFarland (eds.). 1998. Triazine Herbicides: Risk Assessment.

Washington, D.C.: American Chemical Society.

Barke, R.P., and H.C.Jenkins-Smith. 1993. Politics and scientific expertise: Scientists, risk

perception, and nuclear waste policy. Risk Analysis 13:425–439.

Barker, J., M.R.Brown, P.J.Collier, I.Farrell, and P.Gilbert. 1992. Relationship between Legionella

pneumophila and Acanthamoeba polyphaga—Physiological status and susceptibility to

chemical inactivation. Applied and Environmental Microbiology 58(8):2420–2425.

Barnes, D.G., and M.Dourson. 1988. Reference dose (RfD): Description and use in health risk

assessments. Regulatory Toxicology and Pharmacology 8:471–486.

Baxt, W.G. 1995. Application of artificial neural networks to clinical medicine. Lancet 346:1135–

1138.

Beierle, T.C. 1998. Public participation in environmental decisions: An evaluation framework using

social goals. Resources for the Future, November 1998, Discussion Paper 99–06.

Available at http://www.rff.org (November 1998).

REFERENCES 187

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Belosevic, M., R.A.Guy, R.Taghi-Kilani, N.F.Neumann, L.L.Gyurek, L.R. J.Liyanage, P.J.Millard,

and G.R.Finch. 1997. Nucleic acid stains as indicators of Cryptosporidium parvum oocyst

viability. International Journal for Parisitology 27(7):787–798.

Bennett, D.H., T.E.McKone, and W.E.Kastenberg. 2000. Evaluating multimedia chemical

persistence: Classification and regression tree analysis. Environmental Toxicology and

Chemistry 19:810–819.

Bennett, P. 1999. Understanding responses to risk: Some basic findings. Pp. 3–19 in Bennett, P.,

and S.K.Calman (eds.) Risk Communication and Public Health. New York, NY: Oxford

University Press.

Bergh, O., K.Y.Borsheim, G.Bratbak, and M.Heldal. 1989. High abundance of viruses found in

aquatic environments. Nature 340:467–469.

Bergstrom, K., and G.Langeland. 1981. Overleving av spolormegg, salmonel-labakterier og

termostabile koliforme bakterier i jord og pa gronnsaker dyrket i infisert jord [Survival of

ascaris eggs, salmonella and fecal coli in soil and on vegetables grown in infected soil].

Nordisk Veterinaermedicin 33:23–32.

Blaser, M.J., H.L.Hardesty, B.Powers, and W.L.Wang. 1980. Survival of Campylobacter fetus

subsp. jejuni in biological milieus. Journal of Clinical Microbiology 11(4):309–313.

Bodian, D., I.M.Morgan, and H.A.Howe. 1949. Differentiation of three types of poliomyelitis

viruses. III. The grouping of fourteen strains into three immunological types . American

Journal of Hygeine 49:234.

Bogosian, G., L.E.Sammons, P.J.Morris, J.P.O’Neil, M.A.Heitkamp, and D.B.Weber. 1996. Death

of the Escherichia coli K-12 strain W3110 in soil and water. Applied and Environmental

Microbiology 62(11):4114–4120.

Bogosian, G., P.J.Morris, and J.P.O’Neil. 1998. A mixed culture recovery method indicates that

enteric bacteria do not enter the viable but nonculturable state. Applied and Environmental

Microbiology 64(5):1736–1742.

Bohach, G.A., D.J.Fast, R.D.Nelson, and P.M.Schlievert. 1989. Staphylococcal and streptococcal

pyrogenic toxins involved in toxic shock syndrome and related illnesses. Critical Reviews

in Microbiology 17:251.

Bolster, C.H., G.M.Hornberger, A.L.Mills, and J.L.Wilson. 1998. A method for calculating bacterial

deposition coefficients using the fraction of bacteria recovered from laboratory columns.

Environmental Science and Technology 32(10):1329–1332.

Bowie, C., S.Beck, G.Bevan, J.Raftery, F.Silverton, and A.Stevens. 1997. Estimating the burden of

disease in an English region. Journal of Public Health Medicine 19(1):87–92.

Bowker, G.C., and S.L.Star. 1999. Sorting Things Out: Classification and Its Consequences.

Cambridge, Mass.: Massachusetts Institute of Technology Press.

Brayton, P.R., M.L.Tamplin, A.Huq, and R.R.Colwell. 1987. Enumeration of Vibrio cholerae O1 in

Bangladesh waters by fluorescent-antibody direct viable count. Applied and

Environmental Microbiology 53(12):2862–2865.

REFERENCES 188

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Brezonik, P.L. 1990. Principles of linear free-energy and structure-activity relationships and their

applications to the fate of chemicals in aquatic systems. Pp. 113–143 in Stumm, W. (ed.)

Aquatic Chemical Kinetics: Reaction Rates of Processes in Natural Waters. New York:

Wiley-Interscience.

Brown, E.R., V.D.Ojeda, R.Wyn, and R.Levan. 2000. Racial and Ethnic Disparities in Access to

Health Insurance and Health Care. Los Angeles, Calif.: UCLA Center for Health Policy

Research and the Henry J.Kaiser Family Foundation.

Brown, P. 1992. Popular epidemiology and toxic waste contamination: Lay and professional ways

of knowing. Journal of Health and Social Behavior 33:267–281.

Brown, P. 1997. Popular epidemiology revisited. Current Sociology 45:137–156.

BRWG (Biotechnology Research Working Group). 2000. Interagency Report on the Federal

Investment in Microbial Genomics. Washington, D.C.: National Science and Technology

Council.

Buchanan, K.L., and J.W.Murphy. 1998. What makes Cryptococcus neoformans a pathogen?

(synopsis). Emerging Infectious Diseases 4:71–83.

Bukhari, Z, M.M.Marshall, D.G.Korich, C.R.Fricker, H.V.Smith, J.Rosen, and J.L.Clancy. 2000.

Comparison of Cryptosporidium parvum viability and infectivity assays following ozone

treatment of oocysts. Applied and Environmental Microbiology 66:2972–2980.

Bullard, R.D. 1994. Dumping in Dixie: Race, Class, and Environmental Quality. Boulder, Colo.:

Westview Press.

Burton, G.A., Jr., D.Gunnison, and G.R.Lanza. 1987. Survival of pathogenic bacteria in various

freshwater sediments. Applied and Environmental Microbiology 53(4):633–638.

Buswell, C.M., Y.M.Herlihy, L.M.Lawrence, J.T.McGuiggan, P.D.Marsh, C.W.Keevil, and

S.A.Leach. 1998. Extended survival and persistence of Campylobacter spp. in water and

aquatic biofilms and their detection by immunofluorescent antibody and -rRNA staining.

Applied and Environmental Microbiology 64(2):733–741.

Buts, F.I. 1969. O dlitel’nosti vyzhivaniia iaits ascaris lumbricoides v pochve trekh zon Adygeiskoi

avtonomnoi Krasnodarskogo kraia [On the duration of survival of eggs of Ascaris

lumbricoides in the soil of three zones of the Adygei Autonomous District of the

Krasnodar Territory]. Meditsinskaia Parazitologiia i Parazitarnye Bolezni 38:241–242.

Byrd, J.J., H.S.Xu, and R.R.Colwell. 1991. Viable but nonculturable bacteria in drinking water.

Applied and Environmental Microbiology 57(3):875–878.

Cabelli, V.J. 1977. Clostridium perfringens as a water quality indicator. In Hoadley, A., and

B.Dutka (eds.) Bacterial Indicators/Health Hazards Associated with Water. Philadelphia,

Pa.: American Society for Testing and Materials.

REFERENCES 189

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Canale, R.P., M.T.Auer, E.M.Owens, T.M.Heidtke, and S.W.Effler. 1993. Modeling fecal coliform

bacteria—II. Model development and application. Water Research 27(4):703–714.

Caro, A.P.Got, J.Lesne, S.Binard, and B.Baleux. 1999. Viability and virulence of experimentally

stressed nonculturable Salmonella typhimurium. Applied and Environmental Microbiology

65(7):3229–3232.

Chakraborty, T., F.Ebel, E.Domann, K.Niebuhr, B.Gerstel, B.Gerstel, S. Pistor, C.J.Temm-Grove,

B.M.Jockusch, M.Reinhard, U.Walter, and J. Wehland. 1995. A focal adhesion factor

directly linking intracellularly motile Listeria monocytogenes and Listeria ivanovii to the

actin-based cytoskeleton of mammalian cells. European Molecular Biology Organization

Journal 14:1314–1321.

Chamberlin, C., and R.Mitchell. 1978. A decay model for enteric bacteria in natural waters. Chapter

12 in Mitchell, R. (ed.) Water Pollution Microbiology. New York: Wiley-Interscience..

Champliaud, D., P.Gobet, M.Naciri, O.Vagner, J.Lopez, J.Christophe-Buison, I.Varga, G.Harly,

R.Mancassola, and A.Bonnin. 1998. Failure to differentiate C. parvum from C.

meleagridis based on PCR amplification of eight DNA sequences. Applied and

Environmental Microbiology 64(4):1454–1458.

Chang, S.L. 1943. Studies on Entamoeba histolytica, II. Observations concerning encystation,

maturation and excystation of E. histolytica and on the longevity of culture induced cysts

in various fluids and at different temperatures. Journal of Infectious Diseases 72:232–241.

Chapron, C.D., N.A.Ballester, J.H.Fontaine, C.N.Frades, and A.B.Margolin. 2000. Detection of

astroviruses, enteroviruses, and adenovirus types 40 and 41 in surface waters collected and

evaluated by the information collection rule and an integrated cell culture-nested PCR

procedure. Applied and Environmental Microbiology 66:2520–2525.

Chess, C. 2000. Improving public participation in solving environmental health problems. Journal of

Environmental Health 63:24–27.

Chmielewski, R.A., and J.F.Frank. 1995. Formation of viable but nonculturable Salmonella during

starvation in chemically defined solutions. Letters in Applied Microbiology 20(6):380–384.

Colwell, R.R. 1996. Global climate and infectious disease: The cholera paradigm. Science 274

(5295):2025–2031.

Colwell, R.R., and A.Huq. 1994. Vibrios in the environment: Viable but nonculturable Vibrio

cholerae. Pp. 117–133 in Wachsmuth, I.K., P.A.Blake, and O.Olsvik (eds.) Vibrio

cholerae and Cholera: Molecular to Global Perspectives. Washington, D.C.: American

Society for Microbiology.

Conradi, H. 1903. Ueber lösliche durch aseptische Autolyse erhalten Giftstoffe von Ruhr und

Typhusbazillen. Deutsch Med Wochenschr 29:26–28.

Crump, K.S. 1995. Calculation of benchmark doses for continuous data. Risk Analysis 15(1):79–89.

REFERENCES 190

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Dagan, R., and M.A.Menegus. 1986. A combination of four cell types for rapid detection of

enteroviruses in clinical specimens. Journal of Medical Virology 19:219.

Dahinden, U., C.Querol, J.Jager, and M.Nilsson. 1999. Using Computer Models in Participatory

Integrated Assessment—Experiences Gathered in the ULYSSES Project and

Recommendations for Futher Steps. ULYSSES WP-99–2. Available online at: http://

zitl.zit.tu-darmstadt.de/ulysses/index.htm.

Dalkey, N.C. 1969. Analyses from a group opinion study. Futures 1:541–551.

Davies, C.M., J.A.Long, M.Donald, and N.J.Ashbolt. 1995. Survival of fecal microorganisms in

marine and freshwater sediments. Applied and Environmental Microbiology 61(5):1888–

1896.

Davies-Colley, R.J., R.G.Bell, and A.M.Donnison. 1994. Sunlight inactivation of Enterococci and

fecal coliforms in sewage effluent diluted in sea-water. Applied and Environmental

Microbiology 60(6):2049–2058.

Deere, D., J.Porter, R.W.Pickup, and C.Edwards. 1996. Survival of cells and DNA of Aeromonas

salmonicida released into aquatic microcosms. Journal of Applied Bacteriology 81(3):309–

318.

DeRegnier, D.P., L.Cole, D.G.Schupp, and S.L.Erlandsen. 1989. Viability of Giardia cysts

suspended in lake, river and tap water. Applied and Environmental Microbiology 55

(5):1223–1229.

DeRisi, J.L., and V.R.Iyer. 1999. Genomics and array technology. Current Opinion in Oncology

11:76–79.

DiGiovanni, G.D., F.H.Hashemi, N.J.Shaw, F.A.Abrams, M.W. LeChevallier, and

M.Abbaszadegan. 1999. Detection of infectious Cryptosporidium parvum oocysts in

surface and filter backwash water samples by immunomagnetic separation and integrated

cell culture PCR. Applied and Environmental Microbiology 65(8):3427–3432.

DOE (U.S. Department of Energy). 2000. Microbial Genome Project. Available online at: http://

www.sc.doe.gov/production/ober/microbial.

Dowd, S., C.P.Gerba, F.Enriquez, and I.Pepper. 1998a. PCR amplification and species

determination of microsporidia in formalin-fixed feces after immunomagnetic separation.

Applied and Environmental Microbiology 64(1):333–336.

Dowd, S., C.P.Gerba, and I.Pepper. 1998b. Confirmation of the human-pathogenic microsporidia

Enterocytozoon bieneusi, Encephalitozoon intestinalis, and Vittaforma corneae in water.

Applied and Environmental Microbiology 64(9):3332–3335.

Dupray, E., M.P.Caprais, A.Derrien, and P.Fach. 1997. Salmonella DNA persistence in natural

seawaters using PCR analysis. Journal of Applied Microbiology 82(4):507–510.

EDSTAC (Endocrine Disruptor Screening and Testing Advisory Committee). 1999. Final Report:

Volume I. Washington, D.C.: U.S. Environmental Protection Agency.

REFERENCES 191

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Einsiedel, E.F., and D.L.Eastlick. 2000. Consensus conferences as deliberative democracy. Science

Communication 21:323–343.

Enriquez, C.E., et al. 1995. Survival of enteric adenoviruses 40 and 41 in tap, sea and waste water.

Water Research 29(11):2548–2553.

Enzinger, R.M., and R.C.Cooper. 1976. Role of bacteria and protozoa in the removal of Escherichia

coli from estuarine waters. Applied and Environmental Microbiology 31(5):758–763.

EPA (U.S. Environmental Protection Agency). 1995. Guidance for Risk Characterization.

Washington, D.C.: Science Policy Council.

EPA. 1996a. Safe Drinking Water Act Amendments of 1996: General Guide to Provisions.

EPA/810/S/96/001 Washington, D.C.: Office of Water.

EPA. 1996b. The Conceptual Approach for Contaminant Identification (working draft). EPA/812/

D/96/001. Washington, D.C.: Office of Ground Water and Drinking Water.

EPA. 1996c. The Model Plan for Public Participation. Developed by the Public Participation and

Accountability Subcommittee of the National Environmental Justice Advisory Council, a

Federal Advisory Committee to the US EPA. EPA 300-K-96–003. Washington, D.C.:

Office of Environmental Justice.

EPA. 1997a. Announcement of the Draft Drinking Water Contaminant Candidate List; Notice.

Federal Register 62(193):52194–52219.

EPA. 1997b. EPA Drinking Water Microbiology and Public Health Workshop. Washington, D.C.:

Office of Ground Water and Drinking Water.

EPA. 1997c. Meeting Summary: EPA National Drinking Water Contaminant Occurrence Data

Base. Contract No. 68-W4–0001 prepared for Office of Ground Water and Drinking

Water. Washington, D.C.: RESOLVE, Inc.

EPA. 1997d. Options for Developing the Unregulated Contaminant Monitoring Regulation:

Background Document (working draft). EPA/815/D/97/003. Washington, D.C.: Office of

Ground Water and Drinking Water.

EPA. 1998a. Announcement of the Drinking Water Contaminant Candidate List; Notice. Federal

Register 63(40):10274–10287.

EPA. 1998b. Definition of a Public Water System in SDWA Section 1401(4) as Amended by the

1996 SDWA Amendments. Federal Register 63(150):41939.

EPA. 1998c. U.S. EPA Response to Comment Document: Draft Drinking Water Contaminant

Candidate List. Washington, D.C.: Office of Ground Water and Drinking Water.

EPA. 1999a. A Review of Contaminant Occurrence in Public Water Systems. Washington, D.C.

EPA. 1999b. America’s Drinking Water in 1997. EPA 816-F-99–001. Washington, D.C.: Office of

Water.

EPA. 1999c. Final Revisions to the Unregulated Contaminant Monitoring Regulation; Fact Sheet.

Washington, D.C.: EPA, Office of Ground Water and Drinking Water.

REFERENCES 192

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
EPA. 1999d. Issue Papers for Stakeholder Meeting of November 16, 1999. Washington, D.C.:

Office of Ground Water and Drinking Water.

EPA. 1999e. National Drinking Water Advisory Council Factsheet. EPA 816-F-99–009.

Washington, D.C.: Office of Ground Water and Drinking Water.

EPA. 1999f. Revisions to the Unregulated Contaminant Monitoring Regulation for Public Water

Systems; Final Rule. Federal Register 64(180):50556–50620.

EPA. 1999g. Revisions to the Unregulated Contaminant Monitoring Regulation for Public Water

Systems: Proposed Rule. Federal Register 64(83):23397–23458.

EPA. 1999h. Risk Assessment Guidance for Superfund: Volume 3 (Part A, Process for Conducting

Probabilistic Risk Assessment). Draft Revision No. 5. Washington, D.C.

EPA. 1999i. Risk-Based Concentration Table. Region III. Superfund Technical Support.

EPA. 2000a. Current Drinking Water Standards. Available online at http://www.epa.gov/safewater/

mcl.html.

EPA. 2000b. Draft Research Plan for the Drinking Water Contaminant Candidate List. Washington,

D.C.: EPA, Science Advisory Board Review Draft.

EPA. 2000c. Integrated Risk Information System (IRIS); Arsenic, inorganic. Available online at

http://www.epa.gov/iris/subst/0278.htm.

EPA. 2000d. Integrated Risk Information System (IRIS); Nitrate. Available online at http://

www.epa.gov/iris/subst/0076.htm.

EPA. 2000e. Integrated Risk Information System (IRIS); Atrazine. Available online at http://

www.epa.gov/iris/subst/0209.htm.

EPA. 2000f. Integrated Risk Information System (IRIS). Available online at http://www.epa.gov/iris.

EPA. 2000g. National Drinking Water Contaminant Occurrence Database: Introduction. Online.

EPA. Available online at http://www.epa.gov/ncod.

EPA. 2000h. National Drinking Water Contaminant User’s Guide Release Two. EPA. Available

online at: http://www.epa.gov/ncod/html/ncod_userguide.html.

EPA. 2000i. National Primary Drinking Water Regulations; Arsenic and Clarifications to

Compliance and New Source Contaminants Monitoring; Proposed Rules. Federal Register

65(121):38887–38983.

EPA. 2000j. Unregulated Contaminant Monitoring Regulation for Public Water Systems; Analytical

Method for List 2 Contaminants and Clarification; Proposed Rule, Federal Register 65

(178):55361–55398.

EPA. 2000k. Unregulated Contaminant Monitoring for Public Water Systems: Analytical Methods

for Perchlorate and Acetochlor; Announcement of Laboratory Approval and Performance

Testing (PT) Program for the Analysis of Perchlorate; Final Rule and Proposed Rule.

Federal Register 65(42):11372–11385.

ERG (Eastern Research Group, Inc.)-EPA. 2000. Endocrine Disruptor Priority-Setting Database.

Version 2 Beta. Available online at http://www.ergweb.com/endocrine/.

REFERENCES 193

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Favorov, M.O., M.Y.Kosoy, S.A.Tsarev, J.E.Childs, and H.S.Margolis. 2000. Prevalence of

antibody to hepatitis E among rodents in the United States. Journal of Infectious Diseases

181:449–455.

Fields, B.S., E.B.Shotts, J.C.Feeley, G.W.Gorman, and W.T.Martin. 1984. Proliferation of

Legionella pneumophila as an intracellular parasite of the ciliated protozoan Tetrahymena

pyriformis. Applied and Environmental Microbiology 47:467–471.

FIFARS (Federal Interagency Forum on Aging-Related Statistics). 2000. Older Americans 2000.

Hyattsville, Md.

Finkelstein, N.D. 2000. Introduction: Transparency in public policy. Pp. 1–9 in Finkelstein, N.D.

(ed.) Transparency in Public Policy. New York: St Martin’s Press.

Fiorino, D.J. 1990. Citizen participation and environmental risk: A survey of institutional

mechanisms. Science, Technology, and Human Values 15:226–243.

Franz, C.R., and K.G.Jin. 1995. The structure of group conflict in a collaborative work group during

information systems development. Journal of Applied Communication Research 23:108–

127.

Fry, J.C., and D.G.Staples. 1974. The occurrence and role of Bdellovibrio bacteriovirus in a polluted

river. Water Research 8:1029–1035.

Fujioka, R.S., and L.K.Shizumura. 1985. Clostridium perfringens, a reliable indicator of stream

water quality. Journal of the Water Pollution Control Federation 57(10):986–992.

Garson, G.D. 1998. Neural Networks: An Introductory Guide for Social Scientists. Thousand Oaks,

Calif.: Sage Publications.

Gaylor, D., L., Ryan, D.Krewski, and Y.Zhu. 1998. Procedures for calculating benchmark doses for

health risk assessment. Regulatory Toxicology and Pharmacology 28(2):150–164.

Gerba, C.P., et al. 1978. Characterization of sewage solid-associated viruses and behavior in natural

waters. Water Research 12:805–812.

Gibbons, C.L., B.G.Gazzard, M.Ibrahim, Morris-Jones, C.S.L.Ong, and F. M.Awad-El—Kariem.

1998. Correlation between markers of strain variation in Cryptosporidium parvum:

Evidence of clonality. Parasitology Int 47:139–147.

Gibbs, L.M., and CCHW (Citizens Clearinghouse for Hazardous Waste). 1995. Dying from Dioxin.

Boston, Mass.: South End Press.

Gibson, M.C., S.M.deMonsabert, and J.Orme-Zavaleta. 1997. Comparison of noncancer risk

assessment approaches for use in deriving drinking water criteria. Regulatory Toxicology

and Pharmacology 26:243–256.

Gilliom, R.J., and D.R.Helsel. 1986. Estimation of distributional parameters for censored trace level

water quality data. 1. Estimation techniques. Water Resources Research 22(2):135–146.

Golub, M.S. 2000. Adolescent health and the environment. Environmental Health Perspectives

108:355–362.

REFERENCES 194

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Goosney, D.L., D.G.Knoechel, and B.B.Finlay. 1998. Enteropathogenic E. coli, Salmonella and

Shigella: Masters of host cell cytoskeletal exploitation. Emerging Infectious Diseases

5:216–223.

Gouin, T., D.Mackay, and F.Wania. 2000. Screening chemicals for persistence in the environment.

Environmental Science and Technology 34:881.

Haas, C.N., and P.A.Scheff. 1990. Estimation of averages in truncated samples. Environmental

Science and Technology 24:912–919.

Haas, C.N., C.Crockett, J.B.Rose, C.Gerba, and A.Fazil. 1996. Infectivity of Cryptosporidium

parvum oocysts. Journal of the American Water Works Association 88(9):131–136.

Haas, C.N., J.B.Rose, and C.P.Gerba. 1999. Quantitative Microbial Risk Assessment. New York:

John Wiley.

Haas, C.N., A.Thayyar-Madabusi, J.B.Rose, and C.P.Gerba. 2000. Development of a dose-response

relationship for Escherichia coli O157:H7. International Journal of Food Microbiology 56

(2–3):153–159.

Hampton, G. 1999. Environmental equity and public participation. Policy Sciences 32:163–174.

Hattis, D., and E.L.Anderson. 1999. What should be the implications of uncertainty, variability, and

inherent “biases”/“conservatism” for risk management decision-making? Risk Analysis

19:95–107.

Havelaar, A.H., M.During, and J.F.Versteegh. 1987. Ampicillin-dextrin agar medium for the

enumeration of Aeromonas species in water by membrane filtration. Journal of Applied

Bacteriology 62(3):279–287.

Havelaar, A.H., A.E.De Hollander, P.F.Teunis, E.G.Evers, H.J.Van Kranen, J.F.Versteegh, J.E.Van

Koten, and W.Slob. 2000. Balancing the risks and benefits of drinking water disinfection:

Disability adjusted life-years on the scale. Environmental Health Perspectives 108(4):315–

321.

Heidelberg, J.F, J.A.Elsen, W.C.Nelson, R.A.Clayton, M.L.Gwinn, R.J. Dodson, H.H.Haft,

E.K.Hickey, J.D.Peterson, L.Umayam, S.R.Gill, K.E.Nelson, T.D.Read, H.Tettelin,

D.Richardson, M.D.Ermolaeva, J. Vamathevan, S.Bass, H.Qin, I.Dragoi, P.Sellers,

L.McDonald, T.Utterback, R.D.Fleishmann, W.C.Nierman, O.White, S.L.Salzberg,

H.O.Smith, R.R.Colwell, J.J.Mekalanos, J.C.Venter, and C.M.Fraser. 2000. DNA

sequence of both chromosomes of the cholera pathogen Vibrio cholerae. Nature 406:477–

484.

Helsel, D.R., and T.A.Cohn. 1988. Estimation of descriptive statistics for multiply censored water

quality data. Water Resources Research 24(12):1997–2004.

Herrmann, J.E., K.D.Kostenbader, and D.O.Cliver. 1974. Persistence of enteroviruses in water.

Applied Microbiology 28:895–896.

Hinton, G.E. 1992. How neural networks learn from experience. Scientific American 267:144–151.

Hornik, K., M.Stinchombe, and H.White. 1989. Multilayer feedforward networks are universal

approximators. Neural Networks 2:359–366.

REFERENCES 195

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Hurst, C.J., and C.P.Gerba. 1980. Stability of simian rotavirus in fresh and estuarine water. Applied

and Environmental Microbiology 39(1):1–5.

Hyder, A.A., G.Rotllant, and R.H.Morrow. 1998. Measuring the burden of disease: Healthy lifeyears. American Journal of Public Health 88(2):196–202.

IOM (Institute of Medicine). 1988. Medical Technology Assessment Directory. A Pilot Reference

to Organizations, Assessments, and Information Resources. Washington, D.C.: National

Academy Press.

IOM. 1992. Setting Priorities for Health Technologies Assessment: A Model Process. Washington,

D.C.: National Academy Press.

IOM. 1995. Setting Priorities for Clinical Practice Guidelines. Washington, D.C.: National

Academy Press.

IOM. 1999. Toward Environmental Justice: Research, Education, and Health Policy Needs.

Washington, D.C.: National Academy Press.

Islam, M.S., M.K.Hasan, M.A.Miah, G.C.Sur, A.Felsenstein, M. Venkatesan, R.B.Sack, and

M.J.Albert. 1993. Use of the polymerase chain reaction and fluorescent-antibody methods

for detecting viable but nonculturable Shigella dysenteriae type 1 in laboratory

microcosms. Applied and Environmental Microbiology 59(2):536–540.

Jaykus, L.-A. 1997. Epidemiology and detection as options for control of viral and parasitic

foodborne disease. Emerging Infectious Diseases 3:529–539.

Jenkins, M.B., L.J.Anguish, D.D.Bowman, M.J.Walker, and W.C.Ghiorse. 1997. Assessment of a

dye permeability assay for determination of inactivation rates of Cryptosporidium parvum

oocysts. Applied and Environmental Microbiology 63(10):3844–3850.

Jinneman, K.C., J.H.Wetherington, A.M.Adams, J.M.Johnson, B.J.Tenge, N.L.Dang, and W.E.Hill.

1996. Differentiation of Cyclospora sp. and Eimeria spp. by using the polymerase chain

reaction products and restriction fragment length polymorphisms. FDA Lab Information

Bulletin 4044:4.

Johnson, D.W., N.J.Pieniazek, D.W.Griffin, L.Misener, and J.B.Rose. 1995. Development of a PCR

protocol for sensitive detection of Cryptosporidium oocysts in water samples. Applied and

Environmental Microbiology 61(11):3849–3855.

Jothikumar, N., K.Aparna, S.Kamatcdhiammal, R.Paulmurugan, S.Saravandadevi, and P.Khanna.

1993 Detection of hepatitis E virus in raw and treated wastewater with the polymerase

chain reaction. Applied and Environmental Microbiology 59:2558–2562.

Kaplan, J.E., R.A.Goodman, L.B.Schonberger, E.C.Lippy, and G.W.Gary. 1982. Gastroenteritis due

to Norwalk virus: An outbreak associated with a municipal water system. Journal of

Infectious Diseases 146(2):190–197.

Karetnyi, Y.V., M.J.Gilchrist, and S.J.Naides. 1999. Hepatitis E virus infection prevalence among

selected populations in Iowa. Journal of Clinical Virology 14:51–55.

REFERENCES 196

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Kell, D.B., A.S.Kaprelyants, D.H.Weichart, C.R.Harwood, and M.R.Barer. 1998. Viability and

activity in readily culturable bacteria: A review and discussion of the practical issues.

Antonie Van Leeuwenhoek 73(2): 169–187.

Keller, P.E., S.Hashem, L.J.Kangas, and R.T.Kouzes. 1995. Applications of Neural Networks in

Environment, Energy, and Health. Proceedings of the 1995 Workshop on Environmental

and Energy Applications of Neural Networks. River Edge, N.J.: World Scientific

Publishing.

Keusch, G.T. 1998. Shigella. Pp. 1804–1810 In Gorbach, S.L., J.G.Bartlett, and N.R.Blacklow

(eds.) Infectious Diseases. Philadelphia, Pa.: W.B. Saunders.

Keusch, G.T., G.F.Grady, L.J.Mata, and J.M.McIver. 1972. The pathogenesis of Shigella diarrhea.

1. Enterotoxin production by Shigella dysenteriae 1. Journal of Clinical Investigation

51:1212–1218.

Khramtsov, N.V., P.A.Chung, C.C.Dykstra, J.K.Griffiths, U.M.Morgan, M.J.Arrowood, and

S.J.Upton. 2000. Presence of double-stranded RNAs in human and calf isolates of

Cryptosporidium parvum. Journal of Parasitology 86(2):275–282.

Kimbell, L.M., D.L.Miller, W.Chavez, and N.Altman. 1999. Molecular analysis of the 18S rRNA

gene of C. serpentis in a wild-caught corn snake (Elaphe guttata guttata) and a fivespecies restriction fragment length polymorphism-based assay that can additionally discern

C. parvum from C. wrairi. Applied and Environmental Microbiology 65(12):5345–5349.

King, C.H., E.B.Shotts, R.E.Wooley, and K.G.Porter. 1988. Survival of coliforms and bacterial

pathogens within protozoa during chlorination. Applied and Environmental Microbiology

53(12):3023–3033.

Kingombe, C.I., G.Huys, M.Tonolla, M.J.Albert, J.Swings, R.Peduzzi, and T.Jemmi. 1999. PCR

detection, characterization, and distribution of virulence genes in Aeromonas spp. Applied

and Environmental Microbiology 65(12):5293–5302.

Kizeval’ter, I.S., and V.V.Derevitskaia. 1968. Vyzhivaemost’ iaits askarid v pochve v usloviiakh

Moskovskoi oblasti [Survival of ascarid eggs in soil under conditions of the Moscow

region]. Meditsinskaia Parazitologiia i Parazitarnye Bolezni 37:527–529.

Kostrzynska, M., M.Sankey, E.Haack, C.Power, J.E.Aldom, A.H.Chagla, S. Unger, G.Palmateer,

H.Lee, J.T.Trevors, and S.A.De Grandis. 1999. Three sample preparation protocols for

polymerase chain reaction based detection of Cryptosporidium parvum in environmental

samples. Journal of Microbiological Methods 35:65–71.

Kozwich, D., K.A.Johansen, K.Landau, C.A.Roehl, S, Woronoff, and P.A. Roehl . 2000.

Development of a novel, rapid integrated Cryptosporidium parvum detection kit. Applied

and Environmental Microbiology 66(7):2711–2717.

Kransnonos, L.N. 1978. Mnogoletniaia vyzhivaemost’ iaits askarid (Ascaris lumbricoides L., 1758)

V pochve Samarkanda [Long-term survival of asca

REFERENCES 197

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
rid eggs (Ascaris lumbricoides L., 1758) in the soil of Samarkand]. Meditsinskaia

Parazitologiia i Parazitarnye Bolezni 47:103–105.

Kraus, N, T.Malmfors, and P.Slovic. 1992. Intuitive toxicology: Expert and lay judgments of

chemical risks. Risk Analysis 12:215–232.

Kubli, D., R.Steffen, and M.Schär. 1987. Importation of poliomyelitis to industrialized nations

between 1975 and 1984: Evaluation and conclusions for vaccination recommendations.

British Medical Journal 295:169.

Kwo, P.Y., G.G.Schlauder, H.A.Carpenter, P.J.Murphy, J.E.Rosenblatt, G.J.Dawson, E.E.Mast,

K.Krawczynski, and V.Balan. Acute hepatitis E by a new isolate acquired in the United

States. Mayo Clinic Proceedings 72:1133–1136.

Labrique, A.B., D.L.Thomas, S.K.Stoszek, and K.E.Nelson. 1999. Hepatitis E: An emerging

infectious disease. Epidemiologic Reviews 21:162–179.

Laird, F.N. 1993. Participatory analysis, democracy, and technological decision making. Science,

Technology, and Human Values 18:341–361.

Laliberte, P., and D.J.Grimes. 1982. Survival of Escherichia coli in lake bottom sediment. Applied

and Environmental Microbiology 43(3):623–628.

Lancefield, R.C. 1928. The antigenic complex of Streptococcus haemolyticus. I. Demonstration of a

type-specific substance in extracts of Streptococcus haemolyticus. Journal of Experimental

Medicine 47:91–103.

Lander, E.S. 1999. Array of hope. Nature Genetics 21 (Suppl. 1):3–4.

Law, D. 2000. Virulence factors of Escherichia coli O157 and other Shiga toxin-producing E. coli.

Journal of Applied Microbiology 88:729–745.

Laxer, M.A., B.K.Timblin, and R.J.Patel. 1991. DNA sequences for the specific detection of C.

parvum in raw milk by PCR and oligonucleotide probe hybridization. Applied and

Environmental Microbiology 62:3259–3264.

LeChevallier, M.W., W.D.Norton, and R.G.Lee. 1991. Occurrence of Giardia and Cryptosporidium

spp. in surface water supplies. Applied and Environmental Microbiology 57(9):2610–2616.

LeChevallier, M.W., W.Norton, M.Abbaszadegan, T.Atherholt, and J.Rosen. 1997. Variations in

Giardia and Cryptosporidium in Source Water: Statistical Approaches to Analyzing ICR

Data. Denver, Colo.: American Water Works Association.

Lenaghan, J., B.New, and E.Mitchell. 1996. Setting priorities: Is there a role for citizens’ juries?

British Medical Journal 312:1591–1593.

Leng, X., D.A.Mosier, and R.D.Oberst. 1996. Differentiation of C. parvum, C. muris and C. baileyi

by PCR-RFLP analysis of the 18S rRNA gene. Veterinary Parasitology 62:1–7.

Linstone, H., and M.Turoff (eds.). 1975. The Delphi Method: Techniques and Applications.

Reading, Mass.: Addison-Wesley.

Little, R.J.A., and D.B.Rubin. 1987. Statistical Analysis with Missing Data. New York: John Wiley

& Sons.

Lleo, Md., M.C.Tafi, and P.Canepari. 1998. Nonculturable Enterococcus faecalis cells are

metabolically active and capable of resuming active growth. Systematic and Applied

Microbiology 21(3):333–339.

REFERENCES 198

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Lodder, W.J., J.Vinje, R.van de Heide, A.M.de Roda Husman, E.J.T.M. Leenen, and

M.P.G.Koopmans. 1999. Molecular detection of Norwalklike caliciviruses in sewage.

Applied and Environmental Microbiology 65:5624–5627.

Lopez, C.J., and G.M.Gonzalez. 1996. Lay knowledge and public participation in technological and

environmental policy. Philosophy and Technology 2:53–72.

Mahbubani, M., A.K.Bej, M.H.Perlin, F.W.Schaeffer, W.Jakubowski, and R.M.Atlas. 1992.

Differentiation of G. duodenalis from other Giardia spp. by using polymerase chain

reaction and gene probes. Journal of Clinical Microbiology 30:74–78.

Malone, M.J. 1994. Small disagreements: Character contests and working consensus in informal

talk. Symbolic Interaction 17(2):107–127.

Mancini, J.L. 1978. Numerical estimates of coliform mortality under various conditions. Journal of

the Water Pollution Control Federation 50:2477–2484.

Marshall, A., and J.Hodgson. 1998. DNA chips: An array of possibilities. Nature Biotechnology

16:27–31.

Matson, E.A., S.G.Horner, and J.D.Buck. 1978. Pollution indicators and other microorganisms in

river sediment. Journal of the Water Pollution Control Federation (1):13–19.

Mauskopf, J.A., and M.T.French. 1991. Estimating the value of avoiding morbidity and mortality

from foodborne illnesses. Risk Analysis 11(4):619–631.

McCrudden, R., S.O’Connell, T.Farrant, S.Beaton, J.P.Iredale, and D.Fine. 2000. Sporadic acute

hepatitis E in the United Kingdom: An underdiagnosed phenomenon? Gut 46:732–733.

McFeters, G.A., G.K.Bissonnette, J.J.Jezeski, C.A.Thomson, and D.G. Stuart. 1974. Comparative

survival of indicator bacteria and enteric pathogens in well water. Applied Microbiology

27:823–829.

McFeters, G.A., J.S.Kippin, and M.W.LeChevallier. 1986. Injured coliforms in drinking water.

Applied and Environmental Microbiology 51(1):1–5.

Medema, G.J., F.M.Schets, P.F.Teunis, and A.H.Havelaar. 1998. Sedimentation of free and attached

Cryptosporidium oocysts and Giardia cysts in water. Applied and Environmental

Microbiology 64(11):4460–4666.

Melnick, J.L. 1955. Antigenic crossings within poliovirus types. Proceedings of the Society for

Experimental Biology and Medicine 89:131–133.

Melnick, J.L. 1996. Enteroviruses: Polioviruses, coxsackieviruses, echoviruses, and newer

enteroviruses. Chapter 22 in Fields, B.N., D.M.Knipe, and P.M.Howley (eds.) Fields

Virology. Third Edition. Philadelphia, Pa.: Lippincott-Raven.

Melnick, J.L., and C.P.Gerba. 1980. The ecology of enteroviruses in natural waters. CRC Critical

Review in Environmental Control 10:65–93.

REFERENCES 199

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Meng, J., P.Dubreuh, and J.Pillot. 1997. A new PCR based seroneutralization assay in cell culture

for diagnosis of hepatitis E. Journal of Clinical Microbiology 35:1373–1377.

Meng, X-J., P.G.Halbur, M.S.Shapiro, S.Govindarajan, J.D.Bruna, I.K. Mushahwar, R.M.Purcell,

and S.U.Emerson. 1998. Genetic and experimental evidence for cross-species infection by

swine hepatitis E virus. Journal of Virology 72:9714–9721.

Mento, S.J., C.Weeks-Levy, J.M.Tatem, E.J.Gorgacz, and W.F.Waterfield. 1993. Significance of a

newly identified attenuating mutation in Sabin 3 oral poliovirus vaccine. Developments in

Biological Standardization 78:93.

Mohadjer, S., and S.Mehrabian. 1975. Studies on the survival of Shigella flexneri in river and tap

water. Archives Roumaines de Pathologic Experimentales et de Microbiologie 34:307–12.

Morgan, U.M., and R.C.Thompson. 1998. Molecular detection of parasitic protozoa. Parasitology

117(Suppl.):S73–85.

Morgan, U.M., P.T.Monis, R.Fayer, and P.Deplazes. 1999a. Phylogenetic relationships among

isolates of Cryptosporidium: Evidence for several new species. Journal of Parasitology 85

(6):1126–1133.

Morgan, U., L.Xiao, I.Sulaiman, R.Weber, A.A.Lal, R.C.Thompson, and P. Deplazes. 1999b.

Which genotypes/species of Cryptosporidium are human susceptible to? Journal of

Eukaryotic Microbiology 46(5):42S–43S.

Morgan, U., R.Weber, L.Xiao, I.Sulaiman, R.C.Thompson, W.Ndiritu, A.A.Lal, A.Moore, and

P.Deplazes. 2000. Molecular characterization of Cryptosporidium isolates obtained from

human immunodeficiency virus-infected individuals living in Switzerland, Kenya, and the

United States. Journal of Clinical Microbiology 38(3):1180–1183.

Morrow, R.H., and J.H.Bryant. 1995. Health policy approaches to measuring and valuing human

life: Conceptual and ethical issues. American Journal of Public Health 85:1356–1360.

Muela, A., J.M.Garcia-Bringas, I.Arana, and I.Barcina. 2000. The effect of simulated solar radiation

on Escherichia coli: The relative roles of UV-B, UV-A, and photosynthetically active

radiation. Microbial Ecology 39(1):65–71.

Nataro, J.P., and J.B.Kaper. 1998. Diarrheagenic Escherichia coli. Clinical Microbiology Reviews

11(1):142–201

Neal, R.A. 1985. Chemicals and safe drinking water: National and international perspective. Pp. 1–8

(Ch.1) in Rice, R.G. (ed.) Safe Drinking Water: The Impact of Chemicals on a Limited

Resource. Chelsea, Mich.: Lewis Publishers.

Neumann, N.F., L.L.Gyürek, L.Gammie, G.R.Finch, and M.Belosevic. 2000. Comparison of animal

infectivity and nucleic acid staining for assessment of Cryptosporidium parvum viability in

water. Applied and Environmental Microbiology 66(1):406–412.

REFERENCES 200

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Nigrovic, L.E., and V.W.Chiang. 2000. Cost analysis of enteroviral polymerase chain reaction in

infants with fever and cerebrospinal fluid pleocytosis. Archives of Pediatrics and

Adolescent Medicine 154:761–768.

NLM (National Library of Medicine). 2000. TOXNET (Toxicology Data Network). Available

online at http://toxnet.nlm.nih.gov/.

NRC (National Research Council). 1988. Enhancing Human Performance: Issues, Theories, and

Techniques. Washington, D.C.: National Academy Press.

NRC. 1992. In the Mind’s Eye: Enhancing Human Performance. Washington, D.C.: National

Academy Press.

NRC. 1996. Understanding Risk: Informing Decisions in a Democratic Society. Washington, D.C.:

National Academy Press.

NRC. 1997. Safe Water from Every Tap: Improving Water Service to Small Communities.

Washington, D.C.: National Academy Press.

NRC. 1999a. Setting Priorities for Drinking Water Contaminants. Washington, D.C.: National

Academy Press.

NRC. 1999b. Identifying Future Drinking Water Contaminants. Washington, D.C.: National

Academy Press.

NRC. 1999c. Tooele Chemical Agent Disposal Facility: Update on National Research Council

Recommendations. Washington, D.C.: National Academy Press.

Nriagu, J.O. (ed.). 1994. Arsenic in the environment. Part I: Cycling and characterization. Advances

in Environmental Science and Technology Series 26.

O’Brien, R.T., and J.S.Newman. 1977. Inactivation of polioviruses and coxsackieviruses in surface

water. Applied and Environmental Microbiology 33:334–340.

O’Donoghue, P.J. 1995. Cryptosporidium and cryptosporidiosis in man and animals. International

Journal for Parasitology 25(2):139–195.

Okhuysen, P.C., C.L.Chappell, J.H.Crabb, C.R.Sterling, and H.L.DuPont HL. 1999. Virulence of

three distinct Cryptosporidium parvum isolates for healthy adults. Journal of Infectious

Diseases 180(4):1275–1281.

Okun, D.A. Historical overview of drinking water contaminants and public water utilities. Pp. 22–

32 in Identifying Drinking Water Contaminants. Washington, D.C.: National Academy

Press.

Oreskes, N. 1998. Evaluation (not validation) of quantitative models. Environmental Health

Perspectives 106(Suppl. 6):1453–1460.

Orlandi, P.A., and K.A.Lampel. 2000. Extraction-free, filter-based template preparation for rapid

and sensitive PCR detection of pathogenic parasitic protozoa. Journal of Clinical

Microbiology 38:2271–2277.

Pallansch, M.A., and L.J.Anderson. 1998. Coxsackievirus, echovirus, and other enteroviruses.

Chapter 258 in Gorbach, S.L, J.G.Bartlett, and N.R. Blacklow (eds.) Infectious Diseases.

Philadelphia, Pa.: W.B.Saunders.

Palmer, M. 1988. Bacterial loadings from resuspended sediments in recreational beaches. Canadian

Journal of Civil Engineering 15:450–455.

REFERENCES 201

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Pankow, J.F., and J.A.Cherry. 1996. Dense Chlorinated Solvents and Other DNAPLs in

Groundwater: History, Behavior, and Remediation. Portland, Oreg.: Waterloo Press.

Parkin, R.T., and J.M.Balbus. 2000. Variations in concepts of susceptibility in risk assessment. Risk

Analysis 20(5)603–611.

Parkin, R.T., J.O.Davies-Cole, and J.M.Balbus. 2000. Chronic sequelae associated with waterborne

pathogens: A definition and review of human evidence. Paper presented at the Annual

Meeting of the American Public Health Association, Boston, November. Washington,

D.C.: American Public Health Association.

Perhac, R.M. 1998. Comparative risk assessment: Where does the public fit in? Science,

Technology, and Human Values 23:221–241.

Peters, E., and P.Slovic. 1996. The role of affect and worldviews as orienting dispositions in the

perception and acceptance of nuclear power. Journal of Applied Social Psychology

26:1427–1453.

Pina, S., J.Jofre, S.U.Emerson, R.HPurcll, and R.Girones. 1998. Characterization of a strain of

infectious hepatitis E virus isolated from sewage in an area where hepatitis E is not

endemic. Applied and Environmental Microbiology 64:4485–4488.

Pontius, F.W. and S.W.Clark. 1999. Drinking water quality standards, regulations and goals.

Chapter 1 in Water Quality and Treatment: A Handbook of Community Water Supplies,

Fifth Edition. New York: McGraw-Hill.

Quinn, T.C., and R.E.Chaisson. 1998. International epidemiology of the human immunodeficiency

virus. Pp. 1063–1081 in Gorbach, S.L., J.G. Bartlett, and N.R.Blacklow (eds.) Infectious

Diseases. Philadelphia, Pa.: W.B.Saunders Co.

Ravel, J., I.T.Knight, C.E.Monahan, R.T.Hill, and R.R.Colwell. 1995. Temperature-induced

recovery of Vibrio cholerae from the viable but nonculturable state: Growth or

resuscitation? Microbiology 141(Pt. 2):377–383.

Ravetz, J. 1999. Models as metaphors. ULYSSES WP-99–3. Germany: Darmstadt University of

Technology, Center for Interdisciplinary Studies in Technology.

Rayner, S., and R.Cantor. 1987. How fair is safe enough? The cultural approach to societal

technology choice. Risk Analysis 7:3–13.

Redlinger, T., K.O’Rourke, L.Nickey, and G.Martinez. 1998. Elevated hepatitis A and E

seroprevalence rates in a Texas/Mexico border community. Texas Medicine 94:68–71.

Regli, S., J.B.Rose, C.N.Haas, and C.P.Gerba. 1991. Modeling risk for pathogens in drinking water.

Journal of the American Water Works Association 83(11):76–84.

Ren, R.B., E.G.Moss, and V.R.Racaniello. 1991. Identification of two determinants that attenuate

vaccine-related type 2 poliovirus. Journal of Virology 65:1377–1382.

REFERENCES 202

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Renn, O., T.Webler, H.Rakel, P.Dienel, and B.Johnson. 1993. Public participation in decision

making: A three-step procedure. Policy Sciences 26:189–214.

Rigsbee, W., et al. 1997. Detection of the viable but nonculturable state in Escherichia coli

O157:H7. Journal of Food Safety 16:255–262.

Robertson, L.J., A.T.Campbell, and H.V.Smith. 1992. Survival of Cryptosporidium parvum oocysts

under various environmental pressures. Applied and Environmental Microbiology

58:3494–3500.

Rochelle, P.A., D.M.Ferguson, T.J.Handojo, R.De Leon, M.H.Stewart, and R.L.Wolfe. 1997a. An

assay combining cell culture with reverse transcriptase PCR to detect and determine the

infectivity of waterborne Cryptosporidium parvum. Applied and Environmental

Microbiology 63(5):2029–2037.

Rochelle, P.A., R.De Leon, M.H.Stewart, and R.Wolfe. 1997b. Comparison of primers and

optimization of PCR conditions for detection of C. parvum and G. lamblia in water.

Applied Environmental Microbiology 63(1):106–114.

Roivainen, M., M.Knip, H.Hyoty, P.Kulmala, M.Hiltunen, P.Vahasalo, T. Hovi, and

H.K.Akerblom. 1998. Several different enterovirus serotypes can be associated with

prediabetic autoimmune episodes and onset of overt IDDM. Childhood Diabetes in

Finalnd (DiMe) Study Group. Journal of Medical Virology 56:74–78.

Rollins, D.M., and R.R.Colwell. 1986. Viable but nonculturable stage of Campylobacter jejuni and

its role in survival in the natural aquatic environment. Applied and Environmental

Microbiology 52(5):531–538.

Rose, J.B., C.N.Haas, and S.Regli. 1991a. Risk assessment and the control of waterborne giardiasis.

American Journal of Public Health 81:709–713.

Rose, J.B., C.P.Gerba, and W.Jakubowski. 1991b. Survey of potable water supplies for

Cryptosporidium and Giardia. Environmental Science and Technology 25:1393–1400.

Roszak, D.B., and R.R.Colwell. 1987. Survival strategies of bacteria in the natural environment.

Microbiological Reviews 51(3):365–379.

Rowe, G., and L.J.Frewer. 2000. Public participation methods: A framework for evaluation.

Science, Technology, and Human Values 25:3–29.

Sabin, A.B. 1985. Oral poliovirus vaccine: History of its development and use, and current

challenge to eliminate poliomyelitis from the world. Journal of Infectious Diseases 151:420.

Sanchez, J.L., and D.N.Taylor. 1997. Cholera. Lancet 349:1825–1830.

Sawyer, M.H. 1999. Enterovirus infections: Diagnosis and treatment. Pediatric Infectious Diseases

Journal 18:1033–1040.

Schaub, S.A., and R.K.Oshiro. 2000. Public health concerns about caliciviruses as waterborne

contaminants. Journal of Infectious Diseases 181(Suppl. 2):S374–380.

REFERENCES 203

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Selevan, S.G., C.A.Kimmel, and P.Mendoza. 2000. Identifying critical windows of exposure for

children’s health. Environmental Health Perspectives 108(Suppl. 3):451–455.

Shieh, Y.S., R.S.Baric, and M.D.Sobsey. 1997. Detection of low levels of enteric viruses in

metropolitan and airplane sewage. Applied and Environmental Microbiology 63(11):4401–

4407.

Shorter, J. 1973. Correlation Analysis in Organic Chemistry: An Introduction to Linear Free-Energy

Relationships. Oxford: Clarendon Press.

Sibille, I., T.Sime-Ngando, L.Mathieu, and J.C.Block. 1998. Protozoan bacterivory and Escherichia

coli survival in drinking water distribution systems. Applied and Environmental

Microbiology 64(1):197–202.

Slovic, P., T.Malmfors, D.Krewski, C.K.Mertz, N.Neil, and S.Bartlett. 1995. Intuitive toxicology.

II. Expert and lay judgments of chemical risks in Canada. Risk Analysis 15:661–675.

Smith, E.M., C.P.Gerba, and J.L.Melnick. 1978. Role of sediment in the persistence of enteroviruses

in the estuarine environment. Applied and Environmental Microbiology 35(4):685–688.

Smith, J.L. 2001. A reviwe of Hepatitis E virus. Journal of Food Protection 64(4):572–586.

Snacken, R., A.P.Kendal, L.R.Haaheim, and J.M.Woods. 1999. The next influenza pandemic:

Lessons from Hong Kong, 1997. Emerging Infectious Diseases 5:195–203.

Sobsey, M., and B.Olson. 1983. Microbial agents of waterborne disease. In Berger, P.S., and

Y.Argaman (eds.) Assessment of Microbiology and Turbidity Standards for Drinking

Water. Washington, D.C.: U.S. Environmental Protection Agency.

Sowby, F.D. 1965. Radiation and other risks. Health Physics 11:879–887.

Starr, C. 1969. Social benefit versus technological risk. Science 1965:1232–1238.

Steiner, T.S., N.M.Thielman, and R.L.Guerrant. 1997. Protozoal agents: What are the dangers for

the public water supply? Annual Review of Medicine 48:329–340.

Steinert, M., L.Emody, R.Amann, and J.Hacker. 1997. Resuscitation of viable but nonculturable

Legionella pneumophila Philadelphia JR32 by Acanthamoeba castellanii. Applied and

Environmental Microbiology 63 (5):2047–2053.

Stollerman, G.H. 1998. Streptococcus pyogenes (Group A streptococci). Pp. 1703–1719 in Gorbach,

S.L., J.G.Bartlett, and N.R.Blacklow (eds.) Infectious Diseases. Philadelphia, Pa.:

W.B.Saunders.

Strebel, P.M., R.W.Sutter, S.L.Cochi, R.J.Bielik, E.W.Brink, O.M.Kew, M.A.Pallansch,

W.A.Orenstein, and A.R.Hinman. 1992. Epidemiology of poliomyelitis in the United

States one decade after the last reported case of indigenous wild virus-associated disease.

Clinical Infectious Diseaes 14:568–579.

REFERENCES 204

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Sturbaum, G.D., Y.R.Ortega, R.H.Gilman, C.R.Sterling, L.Cabrera, and D.A.Klein. 1998. Detection

of Cyclospora cayetanemsis in wastewater. Applied and Environmental Microbiology 64

(6):2284–2286.

Sulaiman, I.M., L.Xiao, and A.Lal. 1999. Evaluation of C. parvum genotyping techniques. Applied

and Environmental Microbiology 65(10): 4431–4432.

Sulaiman, I. M., U.M.Morgan, R.C.A.Thompson, A.A.Lal, and L.Xiao. 2000. Phylogenetic

relationships of Cryptosporidium parasites based on the 70-kilodalton heat shock protein

(HSP70) gene. Applied and Environmental Microbiology 66:2385–2391.

Swanson, M.B., and A.C.Socha (eds.). 1997. Chemical Ranking and Scoring: Guidelines for

Relative Assessments of Chemicals. Proceedings of the Pellston Workshop on Chemical

Ranking and Scoring. Pensacola, Fla.: Society of Environmental Toxicology and

Chemistry Press.

Swerdlow, D.L., B.A.Woodruff, R.C.Brady, P.M.Griffin, S.Tippen, H.D. Donnell, Jr., E.Geldreich,

B.J.Payne, A.Meyer, Jr., J.G.Wells, et al. 1992. A waterborne outbreak in Missouri of

Escherichia coli O157:H7 associated with bloody diarrhea and death. Annals of Internal

Medicine 117(10):812–819.

Tan, W., X.Fang, J.Li, and X.Liu. 2000. Molecular beacons: A novel DNA probe for nucleic acid

and protein studies. Chemistry: A European Journal 6:1107–1111.

Tesh, S.N. 1999. Citizen experts in environmental risk. Policy Sciences 32:39–58.

Ticehurst, J.R. 1999. Hepatitis E virus. In Murray, P.R., E.J.Baron, M.A. Pfaller, et al. (eds.)

Manual of Clinical Microbiology. Seventh Edition. Washington, D.C.: American Society

for Microbiology.

Tratnyek, P.G. 1998. Correlation analysis of the environmental reactivity of organic substances. Pp.

167–194 in Macalady, D.L. (ed.) Perspectives in Environmental Chemistry. New York:

Oxford University Press.

Tsang, T.H.F., E.K.Denison, H.V.William, L.V.Venczel, M.M.Ginsberg, and D.J.Vugia. 2000.

Acute hepatitis E infection acquired in California. Clinical Infectious Diseases 30:618–619.

Tsarev, S.A., L.N.Binn, P.J.Gomatos, R.R.Arthur, M.K.Monier, H.van-Cryck-Gandre, C.F.Longer,

and B.L.Innis. 1999. Phylogenetic analysis of hepatitis E virus isolates from Egypt. Journal

of Medical Virology 57:68–74.

Tuttle, T.Gomez, M.P.Doyle, J.G.Wells, T.Zhao, R.V.Tauxe, and P.M.Griffin. 1999. Lessons from

a large outbreak of Escherichia coli O157:H7 infections: Insights into the infectious dose

and method of widespread contamination of hamburger patties. Epidemiology and

Infection 122(2):185–192.

Vapnik, V.N. 1995. The Nature of Statistical Learning Theory. New York: Springer-Verlag.

REFERENCES 205

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Vesey, G., N.Ashbolt, E.J.Fricker, D.Deere, K.L.Williams, D.A.Veal, and M.Dorsch. 1998. The use

of a ribosomal RNA targeted oligonucleotide probe for fluorescent labeling of viable

Cryptosporidium parvum oocysts. Journal of Applied Microbiology 85:429–440.

Webler, T., D.Levine, H.Rakel, and O.Renn. 1991. A novel approach to reducing uncertainty: The

group Delphi. Technological Forecasting and Social Change 39:253–263.

Webster, R.G., W.J.Bean, O.T.German, T.M.Chambers, and Y.Kawaoka. 1992. Evolution and

ecology of influenza A viruses. Microbiological Reviews 56:152–179.

Weichart, D., and S.Kjelleberg. 1996. Stress resistance and recovery potential of culturable and

viable but nonculturable cells of Vibrio vulnificus. Microbiology 142(Pt 4):845–53.

Weiss, J.B., H.van Keulen, and T.E.Nash. 1992. Classification of subgroups of Giardia lamblia

based upon ribosomal RNA gene sequence using the polymerase chain reaction. Molecular

Biochemical Parasitology 54:73.

Weiss, S.M., and C.A.Kulikowski. 1990. Computer Systems That Learn: Classification and

Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems.

San Mateo, Calif: Morgan Kaufmann Publishers.

WHO (World Health Organization). 1998. Zoonotic Non-O157 Shiga Toxin Producing Escherichia

coli (STEC). Report of a WHO Scientific Working Group Meeting.

Widmer, G., S.Tzipori, C.Fichtenbaum, and J.K.Griffiths. 1998. Genotypic and phenotypic

characterization of Cryptosporidium parvum isolates from people with AIDS. Journal of

Infectious Diseases 178:834–840.

Widmer, G., E.A.Orbacz, and S.Tzipori. 1999. β-tubulin mRNA as a marker of Cryptosporidium

parvum oocyst viability. Applied and Environmental Microbiology 65:1584–1588.

Wiedenmann, A., P.Krüger, and K.Botzenhart. 1998. PCR detection of Cryptosporidium parvum in

environmental samples—a review of published protocols and current developments.

Journal of Industrial Microbiology and Biotechnology 21:150–166.

Wommack, K.E., and R.R.Colwell. 2000. Virioplankton: Viruses in aquatic ecosystems.

Microbiology and Molecular Biology Reviews 64(1):69–114.

Xiao, L., U.Morgan, J.Limor, A.Escalante, M.Arrowood, W.Shulaw, R. Thompson, R.Fayer, and

A.Lal. 1999a. Genetic diversity within Cryptosporidium parvum and related

Cryptosporidium species. Applied and Environmental Microbiology 65(8):3386–3391.

Xiao, L., L.Escalante, Ch. Yan, I.Sulaiman, A.A.Escalante, R.J.Montali, R. Fayer, and A.A.Lal.

1999b. Phylogenetic analysis of Cryptosporidium parasites based on the small-subunit

rRNA gene locus. Applied and Environmental Microbioliology 65(4):1578–1583.

REFERENCES 206

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Xiao, L., Morgan, U.M., Fayer, R.C.Thompson, and A.A.Lal. 2000. Cryptosporidium systematics

and implications for public health. Parasitology Today 16(7):287–292.

Yearley, S. 1999. Computer models and the public’s understanding of science: A case-study

analysis. Social Studies of Science 29:845–866.

Zupan, J., and J.Gasteiger. 1993. Neural Networks for Chemist: An Introduction. New York: VCH

Publishers.

REFERENCES 207

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Acronyms and Abbreviations

A/E attaching and effacing

AIDS acquired immune deficiency syndrome

AWWA American Water Works Association

AWWARF AWWA Research Foundation

BMD benchmark dose

bp base pair

CCL Drinking Water Contaminant Candidate List

CDC Centers for Disease Control and Prevention

CIM contaminant identification method

COMMPS Combined Monitoring-Based and Modeling-Based Priority

Setting

CPF cancer potency factor

DALY disability adjusted life-year

DNA deoxyribonucleic acid

DOE U.S. Department of Energy

DYNAMEC dynamic mechanism for selecting and prioritizing hazardous

substances

EC European Commission

EDPSD Endocrine Disruptor Priority-Setting Database

EDSTAC Endocrine Disruptor Screening and Testing Advisory

Committee (EPA)

ELISA enzyme-linked immunosorbent assay

EPA U.S. Environmental Protection Agency

FDA U.S. Food and Drug Administration

ACRONYMS AND ABBREVIATIONS 208

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FISH fluorescent in situ hybridization

GRAS generally recognized as safe (FDA)

HEV hepatitis E virus

HIV human immunodeficiency virus

HPC heterotrophic plate count

IARC International Agency for Research on Cancer

IMS immunomagnetic separation

IRIS Integrated Risk Information System (EPA)

LEE locus for enterocytic effacement

LFER linear free energy relationship

LOAEL lowest observed adverse effect level

MCL maximum contaminant level

MCLG maximum contaminant level goal

mRNA messenger ribonucleic acid

MTBE methyl-t-butyl ether

NCHS National Center for Health Statistics

NCOD National Drinking Water Contaminant Occurrence Database

(EPA)

NDWAC National Drinking Water Advisory Council

NIAID National Institute of Allergy and Infectious Diseases

NLM National Library of Medicine

NLV Norwalk-like caliciviruses

NOAEL no observed adverse effect level

NPDWR National Primary Drinking Water Regulation

NRC National Research Council

ORD Office for Research and Development (EPA)

OW Office of Water (EPA)

PCCL preliminary CCL

PCR polymerase chain reaction

POP persistent organic pollutant

PTB persistent, toxic, or liable to bioaccumulate

PWS public water system

ACRONYMS AND ABBREVIATIONS 209

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
QALY quality adjusted life-year

QSAR quantitative structure-activity relationship

RFD reference dose

RFLP (PCR) restriction fragment length polymorphism

RNA ribonucleic acid

rRNA ribosomal RNA

SAB Science Advisory Board (EPA)

SAR structure-activity relationship

SDWA Safe Drinking Water Act

SDWIS Safe Drinking Water Information System

SIX Shiga toxin

TCID tissue culture infectious dose

TIGR Institute for Genomic Research

UCM unregulatored contaminant monitoring

UCMR Unregulated Contaminant Monitoring Regulation (Rule)

USGS U.S. Geological Survey

VAR virulence-activity relationship

VFAR virulence-factor activity relationship

WMG “Waterborne Microbial Genomics” project

ACRONYMS AND ABBREVIATIONS 210

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Appendix A

The European Prioritization Schemes

“COMMPS” AND “DYNAMEC”

INTRODUCTION

This appendix provides an overview of two European rule-based schemes

for identifying and prioritizing substances (primarily chemicals) that may pose

risks to freshwater and marine environments and human health through these

aquatic environments. Although neither scheme exclusively addresses drinking

water contaminants, they are provided to illustrate how the complex and often

contentious task of identifying, ranking, and culling multitudes of substances to

much smaller numbers that will receive regulatory and research consideration

has recently been approached in Western Europe. For example, they serve as

two very clear and relevant examples of how expert judgment is vital and

integral to the design, implementation, and validation of these types of

prioritization schemes. In a broader capacity, several facets of their design can

be compared and contrasted with the chemical prioritization schemes reviewed

in the committee’s first report (NRC, 1999a) and the approach recommend in

this report for the development of future Drinking Water Contaminant

Candidate Lists (CCLs).

COMBINED MONITORING-BASED AND MODELING-BASED

PRIORITY SETTING (COMMPS)

Currently 15 countries belong to the European Union, which is becoming

increasingly important in its role of environmental protection through the

European Commission (EC). Among other requirements, Article 16 of the

(1999) European Parliament and Council Directive requires

APPENDIX A 211

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
the commission to establish a List of Substances based on their risk to the

aquatic environment and to human health through the aquatic environment. To

create such a list expediently, the EC decided that a simplified risk-based

assessment procedure was needed that would account for the intrinsic health

and environmental hazards of substances of concern based on monitoring and

modeling data. More specifically, the approach should consider the aquatic

ecotoxicity and human toxicity of a substance through various aquatic exposure

routes and other related factors that may indicate the possibility of widespread

environmental contamination, such as chemical production volume and use

patterns.

On this basis, the COMMPS procedure was developed in collaboration

with the (German) Fraunhofer Institute for Environmental Chemistry and

Ecotoxicology and subsequently accepted by the European Commission to

establish the priority list. The current Version 2 of COMMPS is based on an

approach that combines automated risk-based ranking with subsequent expert

judgment for the final selection of priority substances. That is, the procedure is

essentially a series of simplified substance-by-substance risk assessments.

(Notably, only chemicals were assessed and included on the first priority list.)

The report, Proposal for a European Parliament and Council Decision

Establishing the List of Priority Substances in the Field of Water Policy (EC,

2000) summarizes the background, design, development, intended uses, and

other related information concerning COMMPS. Further information about the

European Commission and COMMPS is available on the Web at http://

www.europa.eu.int.

In brief, the automated risk-based assessment results in two different types

of ranking lists—one type based on monitored exposure levels and the other on

modeled exposure estimates—which are in turn based on production volumes,

use patterns, environmental distribution, and biodegradation as input

parameters. More specifically, the first use of the COMMPS procedure

comprised the following five steps:

1. Selection of candidate substances for the ranking procedure. For this

step, a “list-based” approach was used in which the original candidate

substances were selected from eight existing official lists and

monitoring programs. (The committee notes that this approach is very

similar to the approach used by the EPA to develop the draft 1998 CCL;

EPA, 1997;NRC, 1999b.)

2. Calculation of exposure scores. In this step, two ranked lists were

established for organic chemicals in the aquatic compartment, one

APPENDIX A 212

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
based on surface water monitoring data and the other on modeling data.

Further lists were established for pollutants adsorbed by sediments and

for metals based exclusively on monitoring data.

3. Calculation of health effect scores. One or more such lists were

established for organic pollutants in the aquatic compartment, for

sediments, and for metals based on test data.

4. Computation of the risk-based priority index. Ranked lists were

calculated by multiplying the exposure and the corresponding health

effects index for each substance. Two lists were ultimately developed

for organic chemicals based on aquatic monitoring and modeling data,

respectively. One list was obtained based on sediment monitoring data,

and several lists were obtained for metals.

5. Recommendation of priority substances. For this purpose, a two-step

procedure was applied. In the first step, the ranked lists were screened to

cull a subset of candidate priority substances from each of the lists.

These highly ranked substances were further screened and reorganized

based on two criteria: (1) the grouping of substances that normally occur

as mixtures and (2) the elimination of candidate substances if their

marketing and use are already severely restricted or prohibited in

Western Europe (i.e., “historic” pollution). In the second step, expert

judgment was used on a substance-by-substance basis to make a final

decision on whether a particular candidate priority substance would be

included or excluded from the list.

In general, candidate substances selected based on monitoring exposure

data were accepted for inclusion in the List of Priority Substances unless there

was strong evidence against their high relative rank. In contrast, substances

taken from the modeling list were accepted only if additional information was

available (e.g., additional monitoring data) that supported the high relative rank

of the substance. In all, 658 substances were compiled and evaluated using this

approach. In June 2000, the European Parliament and Council adopted a total of

32 chemicals (e.g., pesticides, solvents, metals) that were selected and

recommended through use of the COMMPS procedure and expert judgment.

APPENDIX A 213

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
A DYNAMIC MECHANISM FOR SELECTING AND

PRIORITIZING HAZARDOUS SUBSTANCES (DYNAMEC)

The OSPAR Commission was founded as a result of the 1992 Oslo and

Paris (OSPAR) Convention for the protection of the Northeast Atlantic marine

environment. It includes 16 Western European countries together with the

European Community (represented by the European Commission). In addition,

participants and observers from more than two dozen nongovernment

organizations representing various environmental groups and industry also

contribute to OSPAR’s activities. One of the major goals of OSPAR is to

develop programs and measures to identify, prioritize, monitor, and control the

emissions, discharges, and losses of hazardous substances that may reach the

Northeast Atlantic Ocean. In this regard, in 1998 the OSPAR Commission

established an Ad Hoc Working Group on the development of a dynamic

mechanism for selecting and prioritizing hazardous substances (hereafter

referred to as DYNAMEC) to update the existing 1998 OSPAR List of (15)

Chemicals (and groups of related chemicals) for Priority Action.

In brief, the purpose of DYNAMEC is to serve as a tool to enable the

OSPAR Commission—in a transparent manner and using sound information—

to identify and select those hazardous substances that have to be addressed by

the commission as a whole. The tool is then used to determine those hazardous

substances that should be given priority in OSPAR’s activities. In broader

terms, DYNAMEC should help the OSPAR Commission as a first step in the

implementation of its long-term strategy on the elimination of anthropogenic

inputs of hazardous and radioactive substances to the Northeast Atlantic Ocean

“within one generation,” that is, by 2020. The DYNAMEC mechanism consists

of several interrelated steps and procedures that are summarized below and

illustrated in Figure A-1.

The OSPAR report Briefing Document on the Work of the DYNAMEC and

the DYNAMEC Mechanism for the Selection and Prioritisation of Hazardous

Substances (OC, 2000) provides an introduction and description of the

DYNAMEC mechanism and other related information. Further information

about the OSPAR Commission, its policies on hazardous substances, and

DYNAMEC is available on the Internet at www.ospar.org.

APPENDIX A 214

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
FIGURE A-1 Simplified overview of steps and procedures within the

DYNAMEC mechanism and work carried out under DYNAMEC. NOTE: PTB

= substance that is persistent (P), toxic (T), or liable to bioaccumulate (B).

SOURCE: Adapted from EC, 1999.

APPENDIX A 215

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
The Chemical Universe

DYNAMEC considered that there are approximately 250,000 manmade

chemicals in the so-called chemical universe. Thus, it would clearly not be

possible to assess and rank all of these chemicals in a substantive manner.

Moreover, the vast majority would invariably not be of concern in the marine

environment. Therefore, as a first step, DYNAMEC incorporated the chemicals

included in three large and well-established European environmental databases:

(1) the Nordic Substance Database (approximately 18,000 substances); (2) the

Danish Environmental Protection Agency quantitative structure-activity

relationship (QSAR) database (more than 166,000 substances); and (3) the

database of the Netherlands’ BKH/Haskoning report (approximately 180,000

substances) for initial assessment. Thus, DYNAMEC also relied on a list-based

approach for the initial identification of chemicals for subsequent consideration.

Initial Selection of Substances

DYNAMEC identified three intrinsic criteria to assess all the substances

compiled in the initial selection step. The working group then established five

sets of cutoff values (ranging from the most to the least restrictive) to be applied

to these criteria. In brief, the criteria assess whether a substance is persistent (P),

toxic (T), or liable to bioaccumulate (B). However, after taking into account the

overall structure and purpose of DYNAMEC, the least stringent selection

criteria and corresponding cutoff values were ultimately applied to the

hazardous substances under consideration. After establishing and applying the

PTB criteria, the criterion for persistency was developed further to render it

more specific to the marine environment. In a separate validation exercise, the

cutoff criteria were also applied to the 246 substances (or groups of related

substances) included on the OSPAR 1998 List of Candidate Substances. The

outcome of this exercise indicated that only 61 of the substances were identified

as being of possible concern, while the remaining 185 were not—due mainly to

a lack of data and a very low potential for bioaccumulation.

APPENDIX A 216

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
The Safety Net Procedure

Under DYNAMEC, “hazardous substances” refers not only to substances

or groups of related substances that are toxic, persistent, and liable to

bioaccumulate, but also to those that are deemed by OSPAR to require a similar

assessment approach—even if they do not meet the criteria for toxicity,

persistence, and bioaccumulation. To help select substances with an “equivalent

level” of concern, DYNAMEC agreed to supplement the initial selections by a

“safety net” procedure. Specifically, DYNAMEC experts reviewed proposals

from interested parties to include substances on the preliminary List of

Substances of Possible Concern that they felt achieved such an equivalent level

of concern. Thus, several substances were ultimately included on the

preliminary list using this mechanism. The safety net procedure is also intended

to address those substances (e.g., metals, inorganic compounds, endocrine

disruptors) for which the criteria of persistency and bioaccumulation are

generally not applicable.

Quality Assurance/Validation

The results of the initial selection of substances were examined by a group

of experts established by DYNAMEC in order to check the plausibility and

consistency of the substance-specific data and exclude those substances that had

been incorrectly selected.

List of Substances of Possible Concern

The ultimate outcome of the initial selection procedure was a List of

Substances of Possible Concern for the marine environment. However,

DYNAMEC noted that the status of this list is not definite and could change as

further information becomes available and in light of improved knowledge.

Fact Sheets

DYNAMEC decided that fact sheets should be prepared to aid further

assessment of all listed substances of possible concern. These fact

APPENDIX A 217

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
sheets would provide comprehensive but concise background information, such

as physical-chemical properties and production/use volume information (where

available). After producing and distributing the first set of fact sheets,

subsequent work focused on expanding the fact sheets for 80 chemicals and

groups of related chemicals that were later determined to require priority action

(i.e., so-called “selection box” substances described later in this appendix).

DYNAMEC noted that additional related work would be necessary to complete

fact sheets for all remaining substances of possible concern and to help locate

and ascertain relevant data to fill gaps on the existing fact sheets.

Flagging Substances

For a variety of reasons, the substances and groups of related substances

identified by the initial selection, process will give rise to differing levels of

concern. In particular, a given substance may (1) have intrinsic properties

similar to persistent organic pollutants (POPs) and fulfill the most restrictive set

of cutoff points for PTB criteria; (2) have suspected endocrine disrupting

properties; and (3) already be adequately addressed in other forums. Regarding

the latter, OSPAR could then evaluate whether to await the outcome of any

relevant action or to initiate specific OSPAR action. Since DYNAMEC sought

to produce a comprehensive and feasible list of substances that are a threat to

the marine environment, OSPAR agreed that any substances falling into one or

more of these three categories should be “flagged” to ensure consideration in

the revision of the existing List of Chemicals for Priority Action.

Ranking

In order to rank all substances or groups of related substances on the

Preliminary List of Substances of Possible Concern, each was characterized

with respect to its production volumes, use patterns, and/or measured

occurrence in the environment. The level of potential concern for each

substance was assessed through use of an effect score (relative toxicity and

liability to bioaccumulate) and an exposure score (relative level of predicted or

measured occurrence in the environment). The mathematical product of these

two scores was used to help determine the relative risk for each listed substance.

This process included automated

APPENDIX A 218

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
data processing and was followed by expert judgment (e.g., on the basis of

chemical fact sheets). In addition, DYNAMEC decided that calculated exposure

estimations and monitored freshwater concentrations, both for the aquatic phase

and in sediment, should be accounted for in the ranking process.

It is important to note that these ranking algorithms were based on those

that had already been established for use in the previously reviewed COMMPS

procedure. However, some algorithms or weighting factors were modified to

render them more suitable for the marine environment. In some cases,

conservative default values were used when certain substance-specific data

were not known or available. In addition, a significant obstacle that

DYNAMEC had to overcome concerned restricted access to some data on

production/use volumes for certain substances for reasons of confidentiality.

This meant that the application of the ranking algorithms, assessment of the

outcome of the ranking, and the data used could be undertaken and validated

only by a limited number of experts with unrestricted access to the data.

For substances without sufficient information available to carry out the

ranking, further action could not be undertaken until either adequate

information became available or some other approach for determining the status

of such substances was developed. The ranking of the List of Substances of

Possible Concern resulted in four lists:

1. substances associated with marine waters based on measured

environmental concentration and the properties of the substances;

2. substances associated with marine waters based on modeled exposure

scores (in turn based on calculation from production volume and use

patterns);

3. substances associated with marine sediments based on measured

environmental concentration and the properties of the substances; and

4. substances associated with marine sediments based on modeled

exposure scores (in turn based on calculation from production volume

and use pattern).

Although final selection of substances for priority action is ultimately a

policy decision by the OSPAR Commission itself, it was agreed that

DYNAMEC should continue to provide information and expert advice to

support revision of the existing OSPAR List of Chemicals for Priority Action.

APPENDIX A 219

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
“Selection Box” of 80 substances

To facilitate these discussions, a selection box of 80 substances (all

chemicals) was extracted by combining the 48 top-ranked substances from the

four ranked lists (excluding certain substances already included on the 1998

OSPAR List of Chemicals for Priority Action) with all initially selected

substances that could fulfill the most stringent cutoffs for the PTB criteria or

those that were previously flagged as endocrine disruptors.

Grouping of Selection Box Substances

DYNAMEC experts examined the 80 selection box substances on the basis

of their expanded chemical fact sheets and established a basis for grouping

these substances that is described in Table A-1. A complete listing of selection

box substances by group is provided in Appendix 4 of the briefing document for

DYNAMEC (OC, 2000).

Based on these groupings, DYNAMEC recommended that the OSPAR

Commission consider adding the 12 substances included in Groups A and B

when it revises the OSPAR List of Chemicals for Priority Action. Regarding the

20 total Group A and B substances that might be in doubt, DYNAMEC

recommended that they should not presently be considered priority substances.

However, interested parties were invited to provide more reliable data for these

substances in 2000–2001 so that they might be considered with the rest of the

Group A and B substances. DYNAMEC further recommended that the 15

substances in Groups C and D should not be considered as priority substances

unless new data could be provided expeditiously to support their consideration.

Lastly, DYNAMEC recommended that OSPAR consider initiating

monitoring activities with respect to some of the heavily regulated substances in

Group E to help determine whether concentrations observed in the environment

result from historic uses, unintended or by-product emissions and discharges, or

long-range (atmospheric) transport. No recommendations were made in the

DYNAMEC report (EC, 2000) concerning Group F endocrine disruptors;

however, OSPAR has established a separate List of Priority Research and

Development Actions for Endocrine Disruptors within its overall strategy for

hazardous substances.

APPENDIX A 220

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
TABLE A-1 Selection Box Groups

Group Contents Description

A 5(13)a Substances of very high concern (i.e., POP-like substances or

substances with severe PTB profile) and indication of

production, use, or occurrence in the environment

B 7(7)a Other initially selected substances with less severe PTB

profile and indication of use or exposure

C 8 Substances of very high concern (i.e., POP-like substances or

substances with severe PTB profile) but with no indication of

use or exposure

D 7 Other initially selected substances with no indication of use or

exposure

E 20 Substances with PTB properties that are already heavily

regulated or withdrawn from the market

F 6 Endocrine disruptors that do not meet P or B criteria and are

not natural hormones

Drop 7 Substances that do not meet the initial selection criteria and

should be deleted from the Draft Preliminary List of

Substances of Possible Concern

a

These substances were initially selected as a result of reliance on QSAR data or experimental data;

thus, the confidence in the assessment might be in doubt.

SOURCE: Adapted from OC, 2000.

REFERENCES

EC (European Commission). 2000. Proposal for a European Parliament and Council Decision

Establishing the List of Priority Substances in the Field of Water Policy Study on the

Prioritisation of Substances Dangerous to the Aquatic Environment. Document

500PC0047 online at http://europa.eu.int/eur-lex/en/com/dat/2000/en_500PC0047.html.

EPA (U.S. Environmental Protection Agency). 1997. Announcement of the Draft Drinking Water

Contaminant Candidate List; Notice. Federal Register 62(193):52194–52219.

APPENDIX A 221

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
NRC (National Research Council). 1999a. Setting Priorities for Drinking Water Contaminants.

Washington, D.C.: National Academy Press.

NRC. 1999b. Identifying Future Drinking Water Contaminants. Washington, D.C.: National

Academy Press.

OC (OSPAR Commission for the Protection of the Marine Environment of the North-East Atlantic).

2000. Briefing Document on the Work of the DYNAMEC and the DYNAMEC

Mechanism for the Selection and Prioritization of Hazardous Substances. Reference

Number:1998–16.

APPENDIX A 222

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Appendix B

Matlab Programs for Contaminant

Classification

This appendix contains the Matlab1 programs that were used to conduct the

classification exercises described in Chapter 5 of this report.

class_init.m --initialization code

lin_class.m --code to train a linear classifier

nn_class.m --code to train a neural network classifier

class_error.m --code for error analysis

lin_predict.m --code to predict classification using the linear classifier

nn_predict.m --code to predict classification using the neural network classifier

% -------------------------------------------------------------------------

% NRC Committee on Drinking Water Contaminants

% Filename: class_init.m

% Matlab code to initialize the classification

problem.

% Data are loaded and attributes are analyzed.

% After running this, run either lin_class.m or

nn_class.m.

% ------------------------------------------------------------------------

% Load the training data set and set up data

variables

1 Matlab 6 ©The MathWorks, Inc. 3 Apple Hill Drive, Natick, MA 01760–2098; http://

www.mathworks.com/products/matlab/.

APPENDIX B 223

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
S=load(‘caldata.txt’); % the name of the

calibration data file

id=S(:,1) ;

t=S(:,2) ; % class labels (target)

X=S(:,3:7) ; % attributes

fid=fopen(‘caldata_id.txt’,‘r’); % the file

containing the contaminant names

names=[] ;

for i=1:length(t)

if i==1

names=str2mat(fscanf(fid,‘%s’,1)) ;

else

names=str2mat(names, fscanf(fid,‘%s’,1)) ;

end

end

fclose(fid) ;

X1=[] ; X0=[] ;

for i=1:length(t)

if t(i)==1

X1=[X1;X(i,:)] ;

end

if t(i)==0

X0=[X0;X(i,:)] ;

end

end

aaa=size (X1) ; NT1=aaa(1) ; % The number of

contaminants with T=1

aaa=size(X0); NT0=aaa(1); % The number of

contaminants with T=0

% ----------------------------------------------------------------------

% Plot correlation analysis of attributes

figure(1)

str(1)={‘Severity’} ;

str(2)={‘Potency’} ;

str(3)={‘Prevalence’} ;

str(4)={‘Magnitude’} ;

str(5)={‘Persist/Mob’} ;

fs=12;

for i=1:5

APPENDIX B 224

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
subplot (5,5,i) ,

plot (X1 (: , i), X1 (: , 1), ‘kx’, X0 (:,i), X0 (: , 1), ‘ko’, ‘LineW

idth’, 1)

axis square

set (gca, ‘LineWidth’, 1)

text (0, 12, str(i), ‘FontSize’, fs)

if i==1

text (-3, 0, str(1), ‘Rotation’, 90, ‘FontSize’, fs)

end

end

for i=2:5

subplot (5,5,i+5) ,

plot (X1 (: , i), X1 (: , 2), ‘kx’, X0 (: , i), X0 (: , 2), ‘ko’, ‘LineW

idth’, 1)

axis square

set (gca, ‘LineWidth’, 1)

if i==2

text (-3, 0, str(2), ‘Rotation’, 90, ‘FontSize’, fs)

end

end

for i=3:5

subplot (5,5,i+10) ,

plot (X1(: , i), X1(: , 3), ‘kx’, X0(: , i), X0(: , 3), ‘ko’, ‘LineW

idth’, 1)

axis square

set (gca, ‘LineWidth’, 1)

if i==3

text (-3, 0, str(3), ‘Rotation’, 90, ‘FontSize’, fs)

end

end

for i=4:5

subplot (5,5,i+15) ,

plot (X1(: , i), X1(: , 4), ‘kx’, X0(: , i), X0(: , 4), ‘ko’, ‘LineW

idth’, 1)

axis square

set (gca, ‘LineWidth’, 1)

if i==4

text (-3, 0, str(4), ‘Rotation’, 90, ‘FontSize’, fs)

end

end

for i=5:5

subplot (5,5,i+20) ,

plot (X1(: , i), X1(: , 5), ‘kx’, X0(: , i), X0(: , 5), ‘ko’, ‘LineW

idth’, 1)

axis square

APPENDIX B 225

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
set (gca, ‘LineWidth’, 1)

if i==5

text (-3, 0, str(5), ‘Rotation’, 90, ‘FontSize’, fs)

end

end

% ---------------------------------------------------

% End of program

% ---------------------------------------------------

% ---------------------------------------------------

% NRC Committee on Drinking Water Contaminants

% Filename: lin_class.m

% Matlab code to build a linear classifier on the

training data set.

% After this, run class_error.m and lin_predict.m.

% ------------------------------------------------------

% Linear Regression y=Xw where w is the weight

vector

Xlin=[X ones (length (t) , 1)]; % Add a column of ones

to fit bias/intercept.

X1lin=[X1 ones (NT1, 1)] ; % Add a column of ones to

fit bias/intercept.

X0lin=[X0 ones (NT0, 1)] ; % Add a column of ones to

fit bias/intercept.

w=pinv (Xlin) *t;

disp (‘The weights (five attributes plus offset)

are: ’) ;

disp (w) ;

y=Xlin*w;

y1=X1lin*w;

y0=X0lin*w;

meanse=sum( (y-t) . ^2)/length (t) ;

disp (‘The mean squared error is:’)

disp (meanse) ;

% -------------------------------------------------------

% End of program

APPENDIX B 226

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
% ---------------------------------------------------------------------

% ---------------------------------------------------------------------

% NRC Committee on Drinking Water Contaminants

% Filename: nn_class.m

% Matlab code to build a neural network classifier on

the training data set.

% After this, run class_error.m and nn_predict.m.

% -------------------------------------------------------------------

% Set up Neural Network with two feed forward layers.

% The first is a hidden layer containing two nodes.

% The second is an output layer with a single node.

% Both layers have biases.

% The hidden layer has a hyperbolic tangent sigmoid

transfer function.

% The output layer has a linear transfer function.

% The training algorithm uses a conjugate gradient

search method.

% Network performance is measured according to the

mean of squared errors.

figure(2)

Xminmax=[1 10; 1 10; 1 10; 1 10; 1 10] ;

tranfuns={‘tansig’ ‘purelin’};

net=newff (Xminmax, [2 1] , tranfuns, ‘traincgb’,

‘learngdm’, ’mse‘) ;

net.trainParam.min_grad=1e-10;

net.trainParam.epochs=1000000;

net.trainParam.minstep=1.0e-10;

net=train(net,X’, t’);

disp (‘Input weight matrix, bias, and transfer

function in first layer’);

net.IW{1, 1}

net.b{1}

net.layers{1}.transferFcn

if net.numLayers>1

for i=2:net.numLayers

disp (‘Layer weight matrix, bias, and transfer

function for next layer’);

net.LW{i,i-1}

APPENDIX B 227

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
net.b{i}

net.layers{i}.transferFcn

end

end

y=sim(net,X’) ; y=y’ ;

y1=sim (net, X1’); y1=y1’ ;

y0=sim(net, X0’) ; y0=y0’ ;

% ----------------------------------------------------------------

% End of program

% ----------------------------------------------------------------

% ----------------------------------------------------------------

% NRC Committee on Drinking Water Contaminants

% Filename: class_error.m

% Matlab code to determine classification error and

optimize the threshold.

% After this, run either lin_predict.m or

nn_predict.m.

% -------------------------------------------------------------------

% Classification error in training data set

minthresh=min(0, min(min(y1), min(y0)));

int=.05;

Threshrange=minthresh:int:max(max(y1) , max(y0));

idxZero=(t==0) ;

idxOne=(t>0) ;

E0=[] ; E1=[] ;

N0misclass=[] ; N1misclass=[] ; Nmisclass=[] ;

for thresh=Threshrange

classOne=(y>thresh) ;

classZero=(y<=thresh);

N0mc=sum(idxZero & classOne) ;

N1mc=sum(idxOne & classZero) ;

Nmc=N0mc+N1mc;

N0misclass=[N0misclass N0mc]; %The number of

T=0 misclassified

N1misclass=[N1misclass N1mc]; %The number of

T=1 misclassified

APPENDIX B 228

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Nmisclass=[Nmisclass Nmc]; %The total number misclassified

e00=N0mc/sum(idxZero) ;

e11=N1mc/sum(idxOne) ;

E0=[E0 e00] ; % The fraction of T=0 contaminants that are misclassified

as 1

E1=[E1 e11] ; % The fraction of T=1 contaminants that are misclassified

as 0

end

figure(3)

plot (Threshrange, 100*E0, ‘ko--

’, Threshrange, 100*E1, ‘kx:’, ‘Markersize’, 8, ‘LineWidth’

, 1.5)

set (gca, ‘LineWidth’, 2, ‘fontsize’, fs);

xlabel (‘Threshold’, ‘FontSize’, fs)

ylabel (‘Classification Error (%)’, ‘FontSize’, fs)

legend (‘error for T=0 contaminants’, ‘error for T=1

contaminants’, 0)

figure(4)

plot (Threshrange, N0misclass, ‘ko--

’, Threshrange, N1misclass, ‘kx:’, Threshrange, Nmisclass,

‘k + `’, ‘Markersize’, 8, ‘LineWidth’, 1.5)

set (gca, ‘LineWidth’, 2, ‘fontsize’, fs);

xlabel (‘Threshold’, ‘FontSize’, fs)

ylabel (‘Classification Error (number that are

misclassified)’, ‘FontSize’, fs)

legend (‘number of misclassified T=0

contaminants’, ‘number of misclassified T=1

contaminants’, ‘total number of misclassified

contaminants’, 0)

% ----------------------------------------------------------------------

% Find the threshold that minimizes the total number

of misclassified contaminants

inda=find(Nmisclass==min(Nmisclass)) ;

threshes=Threshrange(inda) ;

sthreshes=size(threshes) ;

if sthreshes(2)>1 % If there are more than one

threshold values...

indb=

find(E0(inda)+E1(inda)==min(E0(inda)+E1(inda))) ;

thresh=threshes(indb); %...fine the one the minimizes the total percent error

APPENDIX B 229

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
else

thresh=threshes;

end

sthresh=size(thresh);

if sthresh(2)>1 % If there are still more than

one threshold values...

thresh=min(thresh); %...choose the smallest one.

end

disp (‘The optimal threshold is:’) ;

disp (thresh) ;

indc=find(Threshrange==thresh) ;

disp (‘The percent error in misclassifying T=1

contaminants is:’) ;

disp (100*E1(indc)) ;

disp (‘The percent error in misclassifying T=0

contaminants is:’) ;

disp (100*E0(indc)) ;

disp (‘The total number of misclassified contaminants

is:’) ;

disp (Nmisclass(indc)) ;

mis_y1=find(y1<thresh) ;

mis_y0=find(y0>thresh) ;

disp (‘Misclassified T=1 contaminants are:’) ;

for i=1:N1misclass(indc)

disp (names(mis_y1(i),:)) ;

disp ([mis_y1(i), y1(mis_y1(i))]) ;

end

disp (‘Misclassified T=0 contaminants are:’) ;

for i=1:N0misclass(indc)

disp (names(NT1+mis_y0(i),:)) ;

disp ([mis_y0 (i), y0 (mis_y0 (i))]) ;

end

% --------------------------------------------------------------

% Plot classification results as a histogram

figure(5)

fs=12;

T1col=‘w’; T0col=‘k’;

histax=Threshrange+int;

[n,xout]=hist(y1,histax) ;

bar (xout,n,.4, T1col) ;

APPENDIX B 230

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
h=findobj (gca, ‘Type’, ‘patch’) ;

set (h, ‘LineWidth’, 2)

if max(n)>30

set (gca, ‘ylim’, [0 30])

upval=num2str(max(n)) ;

text (1.025, 29, ‘\uparrow’) ;

text (1.025, 27.5, upval) ;

else

yset=max(n)+1;

set (gca, ‘Ylim’, [0 yset]) ;

end

hold on

[n,xout]=hist(y0,histax-int/2) ;

bar (xout, n,.4, T0col)

xlabel (‘ {\itY}_{\iti} ’, ‘FontSize’, fs)

ylabel (‘Number of contaminants’, ‘FontSize’, fs)

set (gca, ‘LineWidth’, 2, ‘fontsize’, fs) ;

xx=get (gca, ‘xlim’) ;

yy=get (gca, ‘ylim’) ;

line([thresh, thresh], [0,

. 9*yy (2)], ‘color’, ‘k’, ‘LineStyle’, ‘:’, ‘LineWidth’, 2) ;

ymul=.9; ymo=.1;

labxpos=xx(1)+.04*(xx(2)–xx(1)) ;

labypos=.9*yy(2) ;

boxx=[labxpos labxpos; labxpos+int/2 labxpos+int/2;

labxpos+int/2 labxpos+int/2; labxpos labxpos] ;

boxy=[ymul*labypos (ymul+ymo)*labypos; ymul*labypos

(ymul+ymo)*labypos; ymul*labypos+.05*labypos

(ymul+ymo)*labypos+.05*labypos;

ymul*labypos+.05*labypos

(ymul+ymo)*labypos+.05*labypos] ;

patch(boxx(: , 1), boxy(: , 1), T0col)

patch (boxx(: , 2), boxy(: , 2), T1col, ‘linewidth’, 2)

text (labxpos+int, (ymul+ymo)*labypos,

‘T=1’, ‘Verticalalignment’, ‘bottom’, ‘fontsize’, fs)

text (labxpos+int, ymul*labypos,

‘T=0’, ‘Verticalalignment’, ‘bottom’, ‘fontsize’, fs)

strg(1)={‘Classifier \rightarrow’} ;

strg(2)={‘Threshold ’} ;

text (thresh,. 6*yy(2), strg(1), ‘horizontalalignment’, ‘r

ight’, ‘Fontsize’, fs)

text (thresh,. 53*yy(2), strg(2), ‘horizontalalignment’, ‘

right’, ‘Fontsize’, fs)

hold off

% -----------------------------------------------------------------

APPENDIX B 231

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
% End of program

% ------------------------------------------------------------------

% ------------------------------------------------------------------

% NRC Committee on Drinking Water Contaminants

% Filename: lin_predict.m

% Matlab code to predict classification for test

cases using linear classifier.

% Run this after running lin_class.m and

class_error.m.

% ------------------------------------------------------------------

% Prediction for test cases

SP=load (‘testdata.txt’) ; % the name of the data

file containing test cases

idP=SP(: , 1) ;

XP=SP(: , 2:6) ;

XP=[XP ones(length(idP), 1)] ;

YP=XP*w;

disp (‘The predicted values for the test cases are:’) ;

for i=1:length(idP)

disp ([idP(i), YP(i)]) ;

end

% -------------------------------------------------------------------

% End of program

% -------------------------------------------------------------------

% -------------------------------------------------------------------

% NRC Committee on Drinking Water Contaminants

% Filename: nn_predict.m

% Matlab code to predict classification for test

cases using neural network classifier.

% Run this after running nn_class.m and

class_error.m.

% -------------------------------------------------------------------

% Prediction for test cases

APPENDIX B 232

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
SP=load (‘testdata.txt’) ; % the name of the data

file containing test cases

idP=SP(: , 1) ;

XP=SP(: , 2:6) ;

YP=sim (net, XP’) ;

disp (‘The predicted values for the test cases are:’) ;

for i=1:length(idP)

disp ([idP(i) , YP(i)]) ;

end

% ------------------------------------------------------------------

% End of program

% -----------------------------------------------------------------

APPENDIX B 233

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Appendix C

Biographical Information

COMMITTEE MEMBERS

DEBORAH L.SWACKHAMER, Chair, is a professor in the Division of

Environmental and Occupational Health in the School of Public Health at the

University of Minnesota. Her research involves assessment of contaminants in

the environment and associated risks to public health and the environment. She

has published dozens of papers on topics ranging from inventories of xenobiotic

organic compounds in the Great Lakes, to analytical methods for contaminant

detection, to bioaccumulation of organochlorine compounds in fish and

multimedia approaches for modeling human exposure. She has served on the

executive committee of the Division of Environmental Chemistry of the

American Chemical Society, the Board of Directors of the International

Association for Great Lakes Research, and the Science Advisory Committee of

the U.S. Environmental Protection Agency’s (EPA’s) Great Waters program.

She is currently a member of the Science Advisory Board of the International

Joint Commission of the U.S. and Canada. She previously served on the

National Research Council (NRC) Committee on Coastal Oceans. Dr.

Swackhamer received her B.A. in chemistry from Grinnell College in Iowa, and

her M.S. in water chemistry and Ph.D. in oceanography and limnology from the

University of Wisconsin, Madison.

R.RHODES TRUSSELL, Vice Chair, is the lead drinking water technologist

and director for corporate development at Montgomery Watson, Inc. Dr.

Trussell chairs the EPA Science Advisory Board’s Committee on Drinking

Water. He has served on several NRC committees, is currently a member of the

Water Science and Technology Board, and is a member of the National

Academy of Engineering. Dr. Trussell received

APPENDIX C 234

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
his B.S. in civil engineering and his M.S. and Ph.D. in sanitary engineering

from the University of California, Berkeley.

FRANK J.BOVE is a senior epidemiologist for the Epidemiology and

Surveillance Branch of the Division of Health Studies, Agency for Toxic

Substances and Disease Registry. Dr. Bove has published several papers and

reports on the epidemiology of exposure to drinking water contaminants and

related adverse health effects. He received a B.A. in political science and

philosophy from the University of Pennsylvania and an M.S. in environmental

health science and Sc.D. in epidemiology from the Harvard School of Public

Health.

LAWRENCE J.FISCHER is a professor in the Department of Pharmacology and

Toxicology and is the director of the Institute for Environmental Toxicology at

Michigan State University. He serves as chairperson of the Michigan

Environmental Science Board. His primary research interest is biochemical

toxicology. Specific research includes absorption, distribution, metabolism, and

excretion of drugs and chemicals and toxicity of chemicals to the endocrine

pancreas. Dr. Fischer received his B.S. and M.S. in pharmacy from the

University of Illinois and his Ph.D. in pharmaceutical chemistry from the

University of California, San Francisco.

WALTER GIGER is a professor at the ETH Zurich and at the University of

Karlsruhe. He is the director of the Division for Chemical Pollutants at the

Swiss Federal Institute for Environmental Science and Technology, Zurich. His

research, teaching, and consulting activities focus on organic compounds in the

environment and in the geosphere. Research topics include development of

analytical techniques for identification of organic pollutants in drinking water,

wastewater, and natural waters; investigation of sources, occurrences, and fate

of organic pollutants in wastewater and drinking water; and evaluation of

chemical, physical, and biological processes that determine the environmental

fate of chemicals. Dr. Giger received his B.S. and Ph.D. in chemistry from ETH

Zurich.

JEFFREY K.GRIFFITHS is director of the Graduate Programs in Public Health

and an associate professor of family medicine and community health at Tufts

University School of Medicine. His research is focused on the biology and

epidemiology of the emerging waterborne disease cryptosporidiosis and the use

of micronutrients to boost the immune systems of malnourished children. He

currently serves on the

APPENDIX C 235

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
EPA’s National Drinking Water Advisory Council and has represented the

National Association of People with AIDS to the EPA Drinking Water

Microbial Disinfection and Byproducts Committee. Dr. Griffiths received an

A.B. in chemistry from Harvard College, his M.D. from Albert Einstein College

of Medicine, and his M.P.H.&T.M. from the Tulane University School of

Public Health and Tropical Medicine. He is board certified in pediatrics,

internal medicine, and infectious diseases.

CHARLES N.HAAS is the Betz Chair Professor of Environmental Engineering

at Drexel University. He was formerly a professor and acting chair in the

Department of Environmental Engineering at the Illinois Institute of

Technology. His areas of research involve microbial and chemical risk

assessment, hazardous waste processing, industrial wastewater treatment, waste

recovery, and water and wastewater disinfection processes. He has chaired a

number of professional conferences and workshops, has served as a member of

several advisory panels to the EPA, and is currently on an advisory committee

to the Philadelphia Department of Health. Dr. Haas has served on several NRC

committees, including the Committee on the Evaluation of the Viability of

Augmenting Potable Water Supplies with Reclaimed Water and the Committee

to Review the New York City Watershed Management Strategy. He currently

serves on the NRC Committee on Toxicants and Pathogens in Biosolids

Applied to Land. Dr. Haas received a B.S. in biology and an M.S. in

environmental engineering from the Illinois Institute of Technology and a Ph.D.

in environmental engineering from the University of Illinois.

NANCY K.KIM is director of the Division of Environmental Health

Assessment of the New York State Department of Health and an associate

professor in the School of Public Health at the State University of New York,

Albany. Her research interests include chemical risk assessment, exposure

assessment, toxicological evaluations, structure-activity relationships, and

quantitative relationships among toxicological parameters. She received her

B.A. in chemistry from the University of Delaware and her M.S. and Ph.D. in

chemistry from Northwestern University.

DAVID M.OZONOFF is a professor in and chair of the Department of

Environmental Health in Boston University’s School of Public Health. His

research centers on health effects on communities exposed to various kinds of

toxic chemicals, new approaches to understanding the results of small casecontrol studies, and the effects of exposure misclassification

APPENDIX C 236

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
in environmental epidemiology. He has studied public health effects resulting

from exposure to a number of contaminated sites. Dr. Ozonoff received his

M.D. from Cornell University in 1967 and his M.P.H. from the Johns Hopkins

School of Hygiene and Public Health.

REBECCA T.PARKIN is an associate research professor in the Department of

Environmental and Occupational Health in the School of Public Health and

Health Services at the George Washington University. Previously, Dr. Parkin

was director of scientific, professional, and section affairs at the American

Public Health Association and assistant commissioner of the Division of

Occupational and Environmental Health at the New Jersey Department of

Health. Her areas of expertise include environmental epidemiology, public

health policy, risk assessment, and risk communication. She is a former member

of the NRC’s Water Science and Technology Board. Dr. Parkin received her

A.B. in sociology from Cornell University and her M.P.H. in environmental

health and Ph.D. in epidemiology from Yale University.

CATHERINE A.PETERS is an associate professor in the Program of

Environmental Engineering and Water Resources in the Department of Civil

and Environmental Engineering at Princeton University. Her areas of expertise

include environmental chemistry, engineering statistics, and environmental risk

assessment. Her research combines experimental investigation and

mathematical modeling to understand the processes governing the behavior of

organic contaminants that are complex chemical mixtures. She is particularly

interested in tractable mathematical and analytical methods that adequately

describe the chemistry of complex mixtures and provide meaningful

information that can be used in risk assessment. Dr. Peters received her B.S.E.

in chemical engineering from the University of Michigan and her M.S. in civil

engineering and Ph.D. in civil engineering-engineering and public policy from

Carnegie Mellon University.

JOAN B.ROSE is a professor in the College of Marine Science at the

University of South Florida. Her research interests include methods for

detection of pathogens in wastewater and the environment, water treatment for

removal of pathogens, wastewater reuse, and occurrence of viruses and

parasites in wastewater sludge. Dr. Rose served on the NRC Committee on

Wastewater Management for Coastal Urban Areas and the Committee on

Potable Water Reuse. She is currently the vice chair of the NRC’s Water

Science and Technology Board and is a member of the

APPENDIX C 237

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
Board on Life Sciences. Dr. Rose received a B.S. in microbiology from the

University of Arizona, an M.S. in microbiology from the University of

Wyoming, and a Ph.D. in microbiology from the University of Arizona.

PHILIP C.SINGER is a professor in the Department of Environmental

Sciences and Engineering in the School of Public Health at the University of

North Carolina, Chapel Hill, where he is also director of the School’s Drinking

Water Research Center. Dr. Singer was formerly a member of NRC’s Water

Science and Technology Board and served on the Committee on U.S.

Geological Survey Water Resources Research. A member of the National

Academy of Engineering, he has published more than 150 papers and reports

principally concerned with aspects of water chemistry and drinking water

quality. He is currently a member of the Drinking Water Committee of EPA’s

Science Advisory Board. Dr. Singer received his B.S. in civil engineering from

Cooper Union and his M.S. and Ph.D. in environmental sciences and

engineering from Harvard University.

PAUL G.TRATNYEK is an associate professor in the Department of

Environmental Science and Engineering and the Department of Biochemistry

and Molecular Biology at the Oregon Graduate Institute of Science and

Technology. He is also an affiliated scientist with the Center for Coastal and

Land-Margin Research and the Center for Groundwater Research. His research

primarily involves a wide range of oxidation-reduction reactions that can occur

in the environment and the contribution of these reactions to the fate of organic

pollutants. Examples include oxidations by chlorine dioxide and oxidations of

gasoline oxygenates, such as methyl-t-butyl ether (MTBE). Dr. Tratnyek

received his B.A. in chemistry from Williams College and his Ph.D. in applied

chemistry from the Colorado School of Mines.

STAFF

MARK C.GIBSON is a staff officer at the NRC’s Water Science and

Technology Board and the study director for this report. As a research associate,

he has supported this study since its inception and helped prepare the

committee’s first two reports, Setting Priorities for Drinking Water

Contaminants and Identifying Future Drinking Water Contaminants. He is also

study director for the Committee to Improve the USGS National Water Quality

Assessment (NAWQA) Program. Mr. Gibson

APPENDIX C 238

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html
received his B.S. in biology from Virginia Polytechnic Institute and State

University and his M.S. in environmental science and policy in biology from

George Mason University.

ELLEN A.DE GUZMAN is a senior project assistant at the NRC’s Water

Science and Technology Board. She received her B.A. degree from the

University of the Philippines. She has worked with a number of studies

including Watershed Management for Potable Water Supply, Issues in Potable

Reuse, Valuing Ground Water, New Directions in Water Resources Planning

for the U.S. Army Corps of Engineers, and Improving American River Flood

Frequency Analyses. She co-edits the WSTB newsletter and manages the

WSTB homepage.

APPENDIX C 239

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from theoriginal typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot beretained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

Copyright © National Academy of Sciences. All rights reserved.

Classifying Drinking Water Contaminants for Regulatory Consideration 

http://www.nap.edu/catalog/10080.html