Distribution System

Water Quality Monitoring:

Sensor Technology Evaluation 

Methodology and Results

A Guide for Sensor Manufacturers and 

Water Utilities

EPA 600/R-09/076

October 2009

Distribution System Water Quality Monitoring:

Sensor Technology Evaluation Methodology and Results

A Guide for

Sensor Manufacturers and Water Utilities

John S. Hall

Jeffrey G. Szabo

Water Infrastructure Protection Division

National Homeland Security Research Center

Srinivas Panguluri

Greg Meiners

Shaw Environmental & Infrastructure, Inc.

Cincinnati, Ohio

U.S. Environmental Protection Agency

Office of Research and Development

Water Infrastructure Protection Division

National Homeland Security Research Center

Cincinnati, Ohio





	

		

			

		
ii

Disclaimer

The U.S. Environmental Protection Agency (EPA) through its Office of Research and Development funded 

and managed the research described herein under Contract Nos. EP-C-04-034 and EP-C-09-041 with 

Shaw Environmental & Infrastructure, Inc. This document has been reviewed by the Agency but does not 

necessarily reflect the Agency’s views. No official endorsement should be inferred. The U.S. Environmental 

Protection Agency does not endorse the purchase or sale of any commercial products or services.
iii

Foreword

The U.S. Environmental Protection Agency is charged by Congress with protecting the nation’s air, water, 

and land resources. Under a mandate of national environmental laws, the Agency strives to formulate 

and implement actions leading to a compatible balance between human activities and the ability of 

natural systems to support and nurture life. To meet this mandate, the Agency’s Office of Research and 

Development provides data and science support that can be used to solve environmental problems and 

build the scientific knowledge base needed to manage our ecological resources wisely, to understand 

how pollutants affect our health, and to prevent or reduce environmental risks.

In September 2002, the Agency announced the formation of the National Homeland Security Research 

Center. The Center is part of the Office of Research and Development; it manages, coordinates, supports, 

and conducts a variety of research and technical assistance efforts. These efforts are designed to provide 

appropriate, affordable, effective, and validated technologies and methods for addressing risks posed 

by chemical, biological, and radiological terrorist attacks. Research focuses on enhancing our ability to 

detect, respond (through containment, mitigation, and response to public/media), and stabilize (through 

treatment and decontamination) in the event of such attacks.

The Center’s team of scientists and engineers is dedicated to understanding the terrorist threat, 

communicating the risks, and mitigating the results of attacks. Guided by the roadmap set forth in the 

Agency’s Homeland Security Strategy, the Center ensures rapid production and distribution of water 

security related research products.

The Center created the Water Infrastructure Protection Division to perform research in water protection 

areas including: Protection and Prevention, Detection, Containment, Decontamination and Water Treatment 

Mitigation, and Technology Testing and Evaluation. The detection research can be divided into two main 

categories: 1) support for contamination warning systems for timely detection of contamination events 

and 2) confirmation of events through sampling and analysis. This document focuses on online detection 

technologies evaluated at the Agency’s Test and Evaluation Facility in Cincinnati, Ohio. Additional 

information on the Center and its research products can be found at http://www.epa.gov/nhsrc.

Kim R. Fox, Director

Water Infrastructure Protection Division

National Homeland Security Research Center
iv
v

Table of Contents

Disclaimer................................................................................................................................................ ii

Foreword................................................................................................................................................. iii

List of Tables and Figures...................................................................................................................... vii

Acronyms and Abbreviations.................................................................................................................viii

Acknowledgements ................................................................................................................................. x

Notice of Trademarks and Product Names............................................................................................. xi

Executive Summary............................................................................................................................... xii

1.0 Introduction..................................................................................................................................1-1

1.1 Background............................................................................................................................1-1

1.2 Definitions, Representations and Units..................................................................................1-1

1.3 Concept of Operations for Contamination Warning Systems.................................................1-2

1.4 Research Overview................................................................................................................1-4

1.5 Report Outline........................................................................................................................1-4

2.0 Online Detection Equipment and Testing.....................................................................................2-1

2.1 Description of Testing Apparatus ...........................................................................................2-1

2.1.1 Recirculating DSS Loop No. 6 .......................................................................................2-1

2.1.2 Single Pass DSS ............................................................................................................2-2

2.2 Test Contaminants and Water Matrices .................................................................................2-3

2.2.1 Tested Contaminants .....................................................................................................2-3

2.2.2 Test Water Matrix ...........................................................................................................2-4

2.3 Water Quality Measurement ..................................................................................................2-4

2.3.1 Measured Water Quality Parameters..............................................................................2-4

2.3.2 DSS Loop No. 6 Online Instrumentation.........................................................................2-6

2.3.3 Single Pass DSS Online Instrumentation .......................................................................2-6

2.3.4 Single Pass DSS Online Optical Instruments.................................................................2-7

2.4 Data Collection and Analysis .................................................................................................2-7

2.4.1 Data Collection ..............................................................................................................2-7

2.4.2 Data Analysis .................................................................................................................2-7

2.5 Teaming with EPA’s Water Security Initiative .........................................................................2-8

2.6 EPA’s Future Water Quality Sensor Research ......................................................................2-8

3.0 Instrument Setup and Data Acquisition .......................................................................................3-1

3.1 Site-Specific Requirements....................................................................................................3-1

3.1.1 Environmentally Protected Housing...............................................................................3-1

3.1.2 Access for Servicing the Instrumentation ......................................................................3-2

3.1.3 Pressure-controlled Water Supply .................................................................................3-2

3.1.4 Drainage Access............................................................................................................3-3

3.1.5 Power Supply and Electrical Protection .........................................................................3-3

3.1.6 Transmission Media Access...........................................................................................3-4

3.1.7 Source Water Quality Adjustment..................................................................................3-4

3.1.8 Instrument-Specific Accessories....................................................................................3-4

3.2 Calibration Materials/Reagents and Onsite Accessories.......................................................3-5

3.3 Data Acquisition System........................................................................................................3-5

3.3.1 4 to 20 Milliamperes Current Output..............................................................................3-5

3.3.2 Serial Protocols..............................................................................................................3-6

3.3.3 Data Communication Protocols .....................................................................................3-6

3.3.4 SCADA Setup and Poll Rate..........................................................................................3-6

3.3.5 Data Marking .................................................................................................................3-6

3.3.6 Data Transmission and Storage.....................................................................................3-6

3.4 Best Practices for Instrument Setup and Data Acquisition ....................................................3-7
vi

4.0 Testing Procedures and Safety Precautions................................................................................4-1

4.1 Blank/Control Injection...........................................................................................................4-1

4.2 Contaminant Injection Procedures.........................................................................................4-1

4.2.1 Concentration of the Injected Contaminant....................................................................4-1

4.2.2 Duration of Injection.......................................................................................................4-1

4.2.3 Water Main Flow Rate and Injection Rate......................................................................4-1

4.2.4 Neat Compounds Versus Commercial Off-the-Shelf Products ......................................4-2

4.2.5 Wastewater and Ground Water Injections......................................................................4-2

4.3 Testing and Analytical Confirmation.......................................................................................4-2

4.3.1 Testing Confirmation......................................................................................................4-2

4.3.2 Analytical Confirmation..................................................................................................4-2

4.4 Flushing and Baseline Establishment ....................................................................................4-2

4.5 Health and Safety Precautions ..............................................................................................4-3

4.6 Disposal of Contaminated Water From Test Runs .................................................................4-4

4.7 Best Practices for Testing and Safety Precautions.................................................................4-4

5.0 Data Analysis...............................................................................................................................5-1

5.1 Non-Algorithmic Sensor Response Evaluation ......................................................................5-2

5.1.1 Single Pass DSS Data Analysis .....................................................................................5-2

5.1.2 Recirculating DSS Loop No. 6 Data Analysis .................................................................5-3

5.1.3 Edgewood Chemical and Biological Center Test Loop Data Analysis ............................5-5

5.2 Automated Algorithmic Evaluation of Sensor Response........................................................5-6

5.3 Data Analysis Best Practices ................................................................................................5-8

6.0 Operation, Maintenance and Calibration of Online Instrumentation............................................6-1

6.1 Operation and Maintenance Labor Costs ..............................................................................6-1

6.2 Equipment-Specific Maintenance and Consumable Costs ....................................................6-1

6.3 Total Organic Carbon Instrumentation ...................................................................................6-2

6.3.1 Hach astroTOCTM UV Process Total Organic Carbon Analyzer.....................................6-2

6.3.2 Sievers® 900 On-Line Total Organic Carbon Analyzer..................................................6-2

6.3.3 Spectro::lyzerTM/Carbo::lyzerTM .....................................................................................6-2

6.4 Chlorine Instrumentation........................................................................................................6-3

6.4.1 Hach CL-17 Free and Total Chlorine Analyzer...............................................................6-3

6.4.2 Wallace & Tiernan® Depolox® 3 plus............................................................................6-3

6.4.3 YSI 6920DW ..................................................................................................................6-3

6.4.4 Analytical Technology, Inc., Model A15/62 Free Chlorine Monitor ................................6-3

6.4.5 Rosemount Analytical Model FCL..................................................................................6-4

6.5 Conductivity Instrumentation..................................................................................................6-4

6.6 pH/ Oxygen Reduction Potential Instrumentation ..................................................................6-4

6.7 Turbidity..................................................................................................................................6-4

6.8 Dissolved Oxygen ..................................................................................................................6-5

6.9 Other Conventional Water Quality Parameter/Instrumentation ..............................................6-5

6.10 Online Optical Instrumentation...............................................................................................6-5

6.10.1 FlowCAM® .................................................................................................................6-5

6.10.2 Hach FilterTrakTM 660 sc Laser Nephelometer and Hach 2200 PCX Particle Counter....6-5

6.10.3 BioSentry® ................................................................................................................6-6

6.10.4 Spectro::lyzerTM/Carbo::lyzerTM ..................................................................................6-6

6.10.5 ZAPS MP-1.................................................................................................................6-6

6.11 Best Practices and Lessons Learned ....................................................................................6-7

7.0 Bibliography....................................................................................................................................7-1
vii

List of Tables

Table 2.1 Test Contaminant Matrix.....................................................................................................2-4

Table 2.2 Measured Water Quality Parameters .................................................................................2-5

Table 5.1 Parameter-Specific Significant Change Thresholds ...........................................................5-1

Table 5.2 Percent Change in Sensor Parameter Response to Injected Chemical Contaminants in 

Chlorinated Water – Single Pass DSS ...............................................................................5-2

Table 5.3 Normalized, Signal-to-Noise Corrected Sensor Parameter Response to Injected Chemical 

Contaminants in Chlorinated Water – Single Pass DSS ....................................................5-3

Table 5.4 Percent Change in Sensor Parameter Response to Injected Biological Contaminants and 

Growth Media in Chlorinated Water – Single Pass DSS ....................................................5-4

Table 5.5 Normalized, Signal-to-Noise Corrected Sensor Parameter Response to Injected Biological 

Contaminants and Growth Media in Chlorinated Water – Single Pass DSS ......................5-4

Table 5.6 Percent Change in Sensor Parameter Response to Bacillus globigii Injection in 

Chlorinated Water – Single Pass DSS...............................................................................5-4

Table 5.7 Normalized, Signal-to-Noise Corrected Sensor Parameter Response to Bacillus globigii

Injection in Chlorinated Water – Single Pass DSS .............................................................5-4

Table 5.8 Quantitative Sensor Parameter Response Matrix to Contaminants in Chloraminated 

Cincinnati Tap Water...........................................................................................................5-5

Table 5.9 Percent Change in Sensor Parameter Response to Injected Warfare Agents in Chlorinated 

Water – Edgewood Chemical and Biological Center Test Loop .........................................5-6

Table 5.10 Normalized, Signal-to-Noise Corrected Sensor Parameter Response to Injected Warfare 

Agents in Chlorinated Water – Edgewood Chemical and Biological Center Test Loop ......5-6

List of Figures

Figure 1.1 Architecture of the EPA Contamination Warning System (EPA, 2007a).............................1-3

Figure 2.1 Schematic of DSS Loop No. 6............................................................................................2-1

Figure 2.2 DSS Loop No. 6 Sensor Manifold and Instrumentation Rack.............................................2-2

Figure 2.3 Schematic of Single Pass DSS ..........................................................................................2-3

Figure 2.4 Single Pass DSS – Longitudinal View ................................................................................2-3

Figure 2.5 Single Pass DSS – Connecting Pipe Elbows .....................................................................2-3

Figure 2.6 Single Pass DSS – Sampling Ports....................................................................................2-3

Figure 2.7 DSS Loop No. 6 - Online Instrumentation ..........................................................................2-6

Figure 2.8 Single Pass DSS Instrument Panels ..................................................................................2-7

Figure 2.9 Various Single Pass DSS Optical Instruments ...................................................................2-7

Figure 2.10 Technical Associates Radiation Monitoring Device ...........................................................2-7

Figure 2.11 NexSens iSIC Data Acquisition System ............................................................................2-8

Figure 2.12 First Pilot Utility - Water Security Initiative Instrument Panel Type A.................................2-8

Figure 2.13 First Pilot Utility - Water Security Initiative Instrument Panel Type B.................................2-8

Figure 3.1 Single Pass DSS Instrument Panel at 80-foot Sampling Location .....................................3-2

Figure 3.2 Single Pass DSS Instrument Panel at 1,180-foot Sampling Location ................................3-2

Figure 3.3 Example Constant Head Mechanism for Hach 2200 PCX Particle Counter.......................3-3

Figure 3.4 Field Communications Enclosure.......................................................................................3-3

Figure 3.5 Hach astroTOCTM UV Process Total Organic Carbon Analyzer..........................................3-4

Figure 3.6 Sievers® 900 On-Line Total Organic Carbon Analyzer.......................................................3-5

Figure 3.7 SCADA Data Flow Schematic ............................................................................................3-5

Figure 3.8 T&E Facility NexSens iSIC Datalogger ..............................................................................3-7

Figure 4.1 Injection Apparatus for the Single Pass DSS .....................................................................4-1

Figure 4.2 Glyphosate Triplicate Injection Run Results .......................................................................4-3

Figure 4.3 Glyphosate Injections at Varying Concentrations...............................................................4-3

Figure 5.1 CANARY Operation Schematic..........................................................................................5-7
viii

Acronyms and Abbreviations

μm Micrometer

μS/cm Microsiemens/centimeter

°C Degrees Centigrade or Celsius

°F Degrees Fahrenheit

AC Absolute Change

ANL Argonne National Laboratory

AWWA American Water Works Association

AwwaRF American Water Works Association 

Research Foundation

BOD Biological Oxygen Demand

Cl- Chloride ion

CL2 Chlorine

CO2 Carbon dioxide

CWS Contamination Warning System

DCE Data Circuit-Terminating Equipment

DCS Digital Control Systems

DMSO Dimethyl sulfoxide

DO Dissolved Oxygen

DOC Dissolved Organic Carbon

DSS Distribution System Simulator

DTE Data Terminal Equipment

ECBC Edgewood Chemical and Biological 

Center

EDS Event Detection Software

EIA Electronic Industries Alliance

EPA U.S. Environmental Protection Agency

eq. Equivalent

ETV Environmental Technology Verification

ft/sec Foot per second

FTI Frontier Technology, Inc.

GAC Granular activated carbon

GB G-type Nerve Agent (Sarin or 

isopropyl methylphosphonofluoridate)

GCWW Greater Cincinnati Water Works

gpm Gallons per minute

H+ Hydrogen ion (a proton)

HASP Health and Safety Plan

HMI Human-Machine Interface

HOCL- Hypochlorous acid

HPLC High-Performance (or High-Pressure) 

Liquid Chromatography

hr Hour

HSPD Homeland Security Presidential 

Directive

I/O Input and Output 

ICR Inorganic Carbon Remover

IDLH Immediately Dangerous to Life or 

Health

IEC International Electrotechnical 

Commission

IP Internet Protocol

ISE Ion Selective Electrode

iSIC Intelligent Sensor Interface and 

Control

KCN Potassium cyanide

KHP Potassium hydrogen phthalate

mA Milliamperes

MALS Multi-Angle Light Scattering

MCL Maximum contaminant level

mg/L Milligrams per liter

min Minutes

mL Milliliter

mm Millimeter

mNTU Milli-Nephelometric Turbidity Unit

MSD Metropolitan Sewer District

MS2 Male-specific 2

mV Millivolts

N/A Not available or not applicable

NAREL National Air and Radiation 

Environmental Laboratory

NEMA National Electrical Manufacturers 

Association

NH2Cl Chloramine (monochloramine)

NHSRC National Homeland Security Research 

Center

nm Nanometers

NTU Nephelometric Turbidity Unit

O2 Oxygen

O&M Operation and Maintenance

OCL- Hypochlorite ion

ODBC Open Database Connectivity

OGWDW Office of Ground Water and Drinking 

Water

ORP Oxidation Reduction Potential

PC Personal Computer
ix

pH Potential of hydrogen in standard 

units

PLC Programmable Logic Controller

ppm Parts per million

QAPP Quality Assurance Project Plan

ROC Receiver Operating Characteristic

RP-570 RTU Protocol based on IEC 57 Part 

5-1 (present IEC 870) Version 0 or 1

RS-232 Recommended Standard 232

RS-485 Recommended Standard 485

RTUs Remote Terminal Units

Speak Peak sensor value between first in 

contact of the contaminant with the 

sensor until 15 minutes after the initial 

contact

Sbaseline Baseline (mean) sensor value for 

one hour immediately preceding an 

injection test

S/N Signal-to-Noise

SCADA Supervisory Control and Data 

Acquisition

SDWA Safe Drinking Water Act

Shaw Shaw Environmental & Infrastructure, 

Inc.

SNL Sandia National Laboratories

T&E Test & Evaluation

TA Technical Associates

TCP Transmission Control Protocol

TEVA Threat Ensemble Vulnerability 

Assessment

TEVA-SPOT Threat Ensemble Vulnerability 

Assessment - Sensor Placement 

Optimization Tool

TOC Total Organic Carbon

TTEP Technology Testing and Evaluation 

Program

UC University of Cincinnati

UPS Uninterrupted Power Supply

U.S. United States

USB Universal Serial Bus

UV Ultraviolet

UV-Vis Ultraviolet-Visible

UV254 Ultraviolet 254 nanometer wavelength

v/v Volume/Volume Percent

VX V-series Nerve Agent (S-[2-

(diisopropylamino)ethyl]-O-ethyl 

methylphosphonothioate)

WATERS Water Awareness Technology 

Evaluation Research and Security

WDMP Water Distribution Monitoring Panel

WSD Water Security Division

WSi Water Security Initiative

XML eXtensible Markup Language

ZAPS Zero Angle Photon Spectrometer
x

Acknowledgements

The principal authors of this document, titled “Distribution System Water Quality Monitoring: Sensor 

Technology Evaluation Methodology and Results – A Guide for Sensor Manufacturers and Water Utilities,” 

are Mr. John S. Hall, Dr. Jeffrey G. Szabo, Mr. Srinivas Panguluri, P.E., and Mr. Greg Meiners.

The authors wish to acknowledge the contributions of the following individuals and organizations towards 

the development and review of this document:

Technical reviews of the document were performed by:

Mr. Steve Allgeier, U.S. Environmental Protection Agency’s Office of Ground Water and Drinking 

Water

Mr. Stanley States, Pittsburg Water and Sewer Authority

Mr. Alan Roberson, Director of Security and Regulatory Affairs, American Water Works Association

U.S. Environmental Protection Agency National Homeland Security Research Center quality 

assurance review was performed by:

Ms. Eletha Brady-Roberts, Quality Assurance Manager

Illustrations and publication design were performed by:

Mr. James I. Scott, Shaw Environmental & Infrastructure, Inc.

Cover Photo Credits are as follows:

Unknown Rural Town, West Virginia – photograph from archives of Shaw Environmental & 

Infrastructure, Inc.

Water Tower, Florence, Kentucky – photograph by Ms. Jennifer Panguluri

Child drinking water from a tap – photograph of Mr. Ravi Panguluri taken by Ms. Jennifer Panguluri
xi

Notice of Trademarks and Product Names

Trademark or Product Name Manufacturer’s Name, City, State Web Site

1720D Turbidimeter Hach Company, Loveland, Colorado http://www.hach.com

Analytical Technology, Inc. Model A15/62 

Free Chlorine Monitor

Analytical Technology, Inc., Collegeville, Pennsylvania http://www.analyticaltechnology.com

BioSentry® JMAR Technologies, Inc., San Diego, California http://www.jmar.com

FlowCAM® Fluid Imaging Technologies, Yarmouth, Maine http://www.fluidimaging.com

GuardianBlueTM Early Warning System Hach Company, Loveland, Colorado http://www.hach.com

H2O SentinelTM Frontier Technology Inc., Goleta, California http://www.fti-net.com

Hach 2200 PCX Particle Counter Hach Company, Loveland, Colorado http://www.hach.com

Hach astroTOCTM UV Process Total Organic 

Carbon Analyzer 

Hach Company, Loveland, Colorado http://www.hach.com

Hach CL17 Free Chlorine Analyzer Hach Company, Loveland, Colorado http://www.hach.com

Hach CL17 Total Chlorine Analyzer Hach Company, Loveland, Colorado http://www.hach.com

Hach Event MonitorTM Trigger System Hach Company, Loveland, Colorado http://www.hach.com

Hach FilterTrakTM 660 sc Laser Nephelometer Hach Company, Loveland, Colorado http://www.hach.com

Hach/GLI Model C53 Conductivity Analyzer Hach Company, Loveland, Colorado http://www.hach.com

Hach/GLI Model P53 pH/ORP Analyzer Hach Company, Loveland, Colorado http://www.hach.com

Hach Water Distribution Monitoring Panel 

(WDMP) sc

Hach Company, Loveland, Colorado http://www.hach.com

Hydrolab® DS5 Hach Company, Loveland, Colorado http://www.hach.com

Real Kill® Realex Corporation, Spectrum Brands, St. 

souri

Louis, Mis- http://www.spectrumbrands.com

Rosemount Analytical Model FCL Emerson Process Management, Irvine, California http://www.raihome.com

Roundup® The Scotts Company, LLC or its affiliates, Marysville, 

Ohio

http://www.scotts.com

Sievers® 900 On-Line Total Organic Carbon 

Analyzer 

GE Analytical Instruments, Boulder, Colorado http://www.geinstruments.com

Sievers® RL GE Analytical Instruments, Boulder, Colorado http://www.geinstruments.com

Six-CENSETM CENSAR Technologies, Sarasota, Florida http://www.censar.com

Spectro::lyserTM or Carbo::lyserTM scan Messtechnik GmbH, Vienna, Austria http://www.s-can.at

SSS-33-5FT Technical Associates, Canoga Park, California http://www.tech-associates.com

TROLL® 9000 In-Situ® Inc., Ft. Collins, Colorado http://www.in-situ.com

Wallace & Tiernan® Depolox® 3 plus Siemens Water Technologies, Kent, United Kingdom http://www.wallace-tiernan.com

YSI 6600 YSI Inc.,Yellow Springs, Ohio http://www.ysi.com

YSI 6920DW YSI Inc.,Yellow Springs, Ohio http://www.ysi.com

ZAPS MP-1 ZAPS Technologies Inc., Corvallis, Oregon http://www.zapstechnologies.com
xii

Executive Summary

This report, titled “Distribution System Water Quality Monitoring: Sensor Technology Evaluation 

Methodology and Results – A Guide for Sensor Manufacturers and Water Utilities,” provides an overview 

of the U.S. Environmental Protection Agency’s (EPA’s) research results from investigating water quality 

monitoring sensor technologies that might be used to serve as a real-time contamination warning system 

(CWS) when a contaminant is introduced into a drinking water distribution system. EPA’s concept of CWS 

for protecting water distribution systems is discussed in Chapter 1.0. A principal component of such a 

system is online water quality monitoring. 

Based on a review of available online water quality monitoring sensor technologies, an early determination 

was made that it was not technically feasible to accurately identify and quantify the many different types 

of contaminants that could potentially be introduced into the drinking water supply/distribution system. 

Furthermore, because online sensor technologies need to be economically suitable for mass deployment 

within a distribution system, EPA focused its research on identifying sensor technologies that could be 

used to detect anomalous changes in water quality due to contamination event(s). Once a water quality 

anomaly is detected, the water utility operator is alerted, and further actions (e.g., sampling and analysis) 

could be undertaken by the operator to identify and quantify the contaminant if necessary. This report 

focuses on EPA’s research on pilot-scale evaluations of available online water quality monitoring sensor 

instrumentation.

This report first describes the testing apparatus (the recirculating Distribution System Simulator (DSS) 

- Loop No. 6, Single Pass DSS, and the online instrumentation) used for the pilot-scale evaluations at 

the EPA Test and Evaluation Facility in Cincinnati, Ohio (Chapter 2.0). The instrument setup and data 

acquisition specifics are described in Chapter 3.0. The detailed testing procedures and safety precautions 

are described in Chapter 4.0. The data analysis procedures are presented in Chapter 5.0. Operation and 

maintenance specifics for selected instruments are provided in Chapter 6.0. In addition, each chapter 

includes a best practices summary at the end with key points that are designed to deliver the “lessons 

learned” through this research. A bibliography of selected references is included as Chapter 7.0.
1-1

1.0 Introduction

The safety of drinking water supplied to the consumers 

by water treatment plant operators is dependent upon 

many factors: quality of raw water (surface water and/

or ground water), application of appropriate treatment 

technology/disinfection (as needed), and monitoring 

of treated/finished water within the water distribution system network. Appropriate treatment/disinfection technologies for both surface and ground water 

sources are identified by the U.S. Environmental Protection Agency (EPA) in various regulations that were 

promulgated pursuant to the Safe Drinking Water Act 

(SDWA) of 1974 and its amendments. Although the 

treated water leaving a treatment plant typically meets 

EPA’s water quality requirements, the water could undergo transformation within the various distribution 

system components (e.g., storage tanks and pipes), 

which alters the quality, potentially making it unsuitable for human consumption. To address these issues, 

EPA has developed specific regulations that mandate 

periodic monitoring of water quality within distribution systems. 

Research related to water quality monitoring within 

the distribution system has increased significantly 

since the events of September 11, 2001, when improving the security of our nation’s water infrastructure 

became a major priority. Homeland Security Presidential Directive 7 (HSPD-7), issued on December 

17, 2003, established a national policy for federal 

departments and agencies to identify and prioritize 

United States critical infrastructure and to protect 

the infrastructure from terrorist attacks. Thereafter, 

HSPD-9, issued on January 30, 2004, directed EPA to 

“develop robust, comprehensive, and fully coordinated surveillance and monitoring systems, ... that provide early detection and awareness of disease, pest, 

or poisonous agents.” EPA plays a critical role in this 

effort as the lead federal agency for water security. 

Subsequent to these directives, in March 2004, EPA 

released the peer-reviewed Water Security Research 

and Technical Support Action Plan (Action Plan – 

EPA, 2004a), which identified important water security related issues and outlined research and technical support needs to address these issues. In addition, 

the EPA Action Plan identified a list of projects to be 

undertaken in response to the identified needs. Furthermore, the Action Plan identified several products 

proposed to be developed to enhance the security of 

drinking water and wastewater systems. This report is 

one of the products designed to meet the Action Plan 

requirements specified under Section 3.3.d.2 – Standard Operating Procedures and Quality Assurance and 

Control Practices to Guide the Evaluation of Monitoring Technologies and Section 3.3.d.5 – Standard Operating Procedures for Evaluating Monitoring Technologies.

1.1 Background

The analytical methods and water quality sensors used 

to address EPA regulations pursuant to SDWA were 

not designed to address water security threats. Consequently, data necessary to identify a serious threat to 

the water supply caused by either an accidental release 

or by an intentional act might not be captured during 

routine periodic monitoring at drinking water treatment plants and various distribution system locations. 

Over the past five years, as part of the overall Water 

Awareness Technology Evaluation Research and Security (WATERS) program at the EPA Test & Evaluation 

(T&E) Facility in Cincinnati, Ohio, EPA investigated 

online water quality monitoring technologies that might 

be used to achieve the goal of serving as early warning 

indicators to detect contaminant introduction into the 

drinking water supply. The WATERS program testing 

efforts were sponsored by EPA’s National Homeland 

Security Research Center (NHSRC). During this study, 

a variety of commercially available online sensors/instruments were evaluated.

Based on a review of available online water quality 

monitoring sensor technologies, an early determination 

was made that it was not technically feasible to accurately identify and quantify the many different types of 

contaminants that could potentially be introduced into 

a drinking water supply/distribution system. Furthermore, these online technologies needed to be economically suitable for mass deployment within a distribution 

system. Therefore, EPA focused its research to identify 

online sensor technologies that could be used to detect 

anomalous changes in the baseline water quality without specific regard to precision, accuracy or identification of the contaminant. Once an anomaly is detected 

and the water utility operator is alerted, further actions 

(e.g., grab sampling and analysis) could be undertaken 

by the operator to identify and quantify the contaminant whenever possible. This report focuses on EPA’s 

research on pilot-scale evaluations of available online 

water quality monitoring sensor instrumentation.

1.2 Definitions, Representations 

and Units

For the purposes of this document, a “sensor” is defined 

as an electro-mechanical device (e.g., membrane, electrode, or microchip) that measures a physical or chemical characteristic of water and converts it into a “signal” 

or measured value, which is typically processed further 

by an instrument. 
1-2

An instrument is defined as an electro-mechanical device 

(or a collection of electro-mechanical devices) that can 

manipulate (e.g., amplify) a measured output from an associated “sensor” and transmit the measured value (e.g., 

analog or digital output value) to a data acquisition system. 

Some instruments (e.g., optical instruments) perform 

measurements without an associated sensing element (as 

defined in this section) and contain additional devices 

that transmit data. Therefore, in general, an instrument 

is meant to refer collectively to a sensor that provides the 

overall measurement functionality. Furthermore, a “sensor” or “instrument” response is intended to define the 

change in measured or recorded output value of the relevant water quality parameter. The term “equipment” is 

used to refer collectively to electro-mechanical devices 

that might include one or more sensors, instruments, and 

additional appurtenances such as plumbing, data collection and/or recording devices that are necessary to make 

the overall manufactured device functional. Depending 

upon the focus of the discussion, and to improve document readability, the terms “sensor,” “instrument,” and 

“equipment” have been used somewhat interchangeably 

throughout the document. 

The research did not address instrument-specific precision, accuracy, or ability to identify contaminants.

The term “sensor manufacturer(s)” is intended to include instrument manufacturer(s) and vendor(s) who 

might simply resell or repackage a manufactured product. A listing of tested sensor/instrument technologies 

and their associated registered or unregistered trademarks is included under the Notice of Trademarks and 

Product Names (page xi) in this report. The tested equipment referred to in this document was procured over 

time and used for testing during the period of 2003 to 

2008. Subsequent to the testing, there could have been 

design changes and/or improvements to the equipment 

by the manufacturers. These devices might perform differently under the same tested conditions, but could bear 

the same registered or unregistered trademark. 

Neither the authors nor EPA make any representations 

on the usefulness or general performance of these devices outside the context of the testing described in this 

report. The use of these manufacturer-specific names 

and model numbers throughout the document is to promote clarity so that the reader can identify the tested 

equipment. Any rights associated with these registered 

or unregistered trademarks are the sole property of the 

trademark holders. It is recommended that water utilities 

and other researchers apply their own judgment prior to 

choosing any equipment for water quality monitoring.

English standard units that are commonly used by the 

U.S. water utility personnel have been used throughout 

this document. For example, volume is reported in U.S. 

gallons and velocity in feet per second (ft/s). However, 

in keeping with industry usage, contaminant concentrations are reported in metric units, in milligrams per liter 

(mg/L). Unless otherwise stated for computational purposes, the values from the instruments are presented as 

reported in the output from the individual instrument(s) 

without any conversions provided.

1.3 Concept of Operations for 

Contamination Warning 

Systems

Real-time water quality monitoring to control treatment 

process operations has been successfully performed 

at water treatment plants for many years. EPA’s concept for contamination warning systems (CWS) is designed to extend this monitoring approach to multiple 

locations within a water distribution system (Kessler 

et al., 1998; ISLI, 1999; AwwaRF, 2002; Kirmeyer et 

al., 2002; EPA, 2005 a-d; Roberson and Morley, 2005; 

Allgeier et al., 2006; Dawsey et al., 2006). Consequently, baseline water quality conditions can be monitored 

continuously in real-time such that a sudden change in 

water quality parameter(s) can trigger a contamination 

warning. Monitoring baseline water quality parameters 

within the distribution system will also provide multiple benefits of improved water quality closer to the 

point-of-use and additional security for detecting intentional or unintentional contamination events within the 

system. The capital, operational, and maintenance costs 

for CWS will be difficult to sustain unless multiple benefits are identified. For water utilities, it is important 

to first maximize the security benefits by strategically 

placing the selected online monitors in the network and 

utilizing suitable techniques to evaluate the online sensor responses. Therefore, in addition to evaluating online water quality monitoring and sensor technologies, 

EPA has collaborated with various research entities to 

develop two key software tools that provide these functionalities, described below.

EPA, in collaboration with research organizations 

including the Sandia National Laboratories (SNL), 

Argonne National Laboratory (ANL), University of 

Cincinnati (UC) and the American Water Works Association (AWWA), has developed a software program 

referred to as the Threat Ensemble Vulnerability Assessment - Sensor Placement Optimization Tool (TEVA-SPOT). TEVA-SPOT can be used to determine the 

optimum number and locations for monitoring stations 

within a water distribution system. The software allows 

the user to specify a wide range of performance objectives including: 1) Population-based health measures, 

2) Time to detection, 3) Extent of contamination, 4) 
1-3

Routine Operation Consequence Management

Monitoring & Surveillance

Operational Strategy

Online water quality

Sampling & analysis

Enhanced security

Consumer complaints

911 call data

Emergency Medical

Services data

Poison control data

Syndromic

surveillance data

Over-the-counter

medication sales

Public Health

Event Detection

Possible Determination

Utility

data

storage

Credible Determination Confirmed Determination Remediation & Recovery

Is contamination possible?

Public

health

data

storage

Consequence Management Plan

No Yes

Is contamination

credible?

Yes

No

Drinking water utility

event detection and

initial trigger

validation

Public health event

detection and trigger

validation

Response actions

Operational response

Public health response

Site Characterization

Laboratory confirmation

Risk communication

Is contamination

confirmed?

Yes

Response actions

Operational response

Public health response

Expanded sampling

Laboratory confirmation

Risk communication

Planning

System characterization

Remedy

selection

Remedial

action

Post-remediation

activity

Risk communication

Return to routine monitoring & surveillance

No

Volume of contaminated water consumed, and 5) Number of contamination events detected. TEVA-SPOT facilitates interactive design of a water quality monitoring system by allowing the user to specify constraints

to ensure that the performance objective is satisfied.

For example, a TEVA-SPOT user can integrate expert

knowledge during the design process by identifying either existing or unfeasible sensor locations. Installation

and maintenance costs for sensor placement can also

be factored into the analysis. More information on the

TEVA Research Program and SPOT can be obtained

online at: http://www.epa.gov/nhsrc/water/teva.html.

EPA, in collaboration with SNL, also developed the

CANARY algorithm to evaluate water quality sensor responses and identify changes in water quality that could

indicate a contamination event. The name CANARY is

not an acronym, but suggests a parallel with the historic

“canary in the coal mine” event detection approach in

which the coal miners used canaries to detect poison gas

events. Similarly, the CANARY software evaluates realtime water quality data obtained from various instruments and uses mathematical and statistical techniques

to identify the onset of anomalous water quality events.

The CANARY software allows for the following: 1) the

use of a standard data format for input and output of

water quality and operations data, 2) the ability to select different detection algorithms (the program contains

three different mathematical approaches for analyzing

the data), 3) the ability to select various water utility and

location-specific configuration options, 4) an online operations mode and an off-line evaluation/training mode,

and 5) the ability to generate data needed to establish

performance metrics (e.g., false alarm rates). This algorithmic approach enhances the detection sensitivity of

the field equipment and simultaneously reduces the false

positive alarm events. CANARY is freely available for 

download through the EPA website. More information 

on CANARY can be obtained online at: http://www.epa.

gov/nhsrc/water/teva.html.

Regardless of the approach used by the utility to evaluate the data collected from online sensors, establishing 

a protocol to verify and respond to alarms triggered 

by the online water quality monitoring instruments is 

important. Note that online water quality monitoring 

represents only one component of a holistic CWS. Additional data inputs from the utility and public health 

agencies should be collected and evaluated to complement the benefits of online water quality monitoring 

(See Figure 1.1).

EPA’s Office of Ground Water and Drinking Water 

(OGWDW), Water Security Division (WSD), has fielddeployed a pilot project called the Water Security Initiative (WSi), that is based upon the concepts identified in 

Figure 1.1. The WSi program is being implemented in 

the following three phases: 

• Phase I: develop the conceptual design of a 

system for timely detection and appropriate 

response to drinking water contamination 

incidents to mitigate public health and economic 

impacts;

• Phase II: test and demonstrate CWS 

through pilots at drinking water utilities and 

municipalities and make refinements to the 

design based upon pilot results; and

• Phase III: develop practical guidance and 

outreach to promote voluntary national adoption 

of effective and sustainable drinking water CWS.

Figure 1.1 Architecture of the EPA Contamination Warning System (EPA, 2007a)
1-4

Based on information collected from the ongoing Phase 

I and Phase II activities, WSD has developed a variety 

of guidance and interim guidance documents on related topics including: WaterSentinel system architecture 

(EPA, 2005c), planning for CWS deployment (EPA, 

2007b), developing an operational strategy for CWS 

(EPA, 2008a), developing consequence management 

plans (EPA, 2008b), and the Cincinnati pilot post-implementation system status (EPA, 2008c). In addition, WSD 

had previously developed a modular response protocol 

toolbox to assist water utilities for planning and responding to contamination threats (EPA, 2004[c through j]). 

More information on the EPA WSi can be obtained online at: http://www.epa.gov/watersecurity.

1.4 Research Overview

The vast majority of the research described in this report was conducted at the EPA T&E Facility in Cincinnati, Ohio. Since the early 1990’s, at this facility, EPA 

has conducted research using simulated drinking water 

distribution systems. A number of pilot-scale distribution system simulators (DSSs) are in use at the T&E Facility. EPA operates, maintains, and modifies the DSSs 

as needed to accommodate evolving study designs. For 

the research results reported in this document, EPA employed two types of DSSs at the T&E Facility to investigate water quality monitoring sensor technologies that 

might be used to serve as a real-time early warning system when a contaminant is introduced into the drinking water supply. Only online sensors were evaluated, 

because the response time is critical for achieving the 

project objective of contamination warning. 

To evaluate the selected sensors, a series of test runs was 

conducted by injecting known quantities of potential 

contaminants into the selected DSSs. After injection, 

sensor data were collected continuously and electronically archived. After injection, grab samples were collected periodically to confirm the sensor results. These 

studies were focused on providing independent third 

party data to decision makers in the following areas:

1. What water quality parameters will be most 

useful in CWS?

2. Can online water quality sensors be used 

to reliably trigger alarms in response to 

contamination events within a water distribution 

system?

3. What are the operational and maintenance costs 

associated with online water quality monitoring 

systems?

1.5 Report Outline

The following chapters of this report summarize the 

findings related to this research. Chapter 2.0 presents 

a summary of the various online detection sensors/

instrumentation evaluated and the evaluation-specific 

research activities performed at the EPA T&E Facility 

in Cincinnati, Ohio and other field locations. Chapter 

3.0 describes general instrument setup and data acquisition. Chapter 4.0 contains a description of the testing 

procedures and safety precautions. Chapter 5.0 outlines 

the data analysis procedures. Chapter 6.0 describes the 

operation and maintenance (O&M) and calibration requirements of the tested instrumentation. At the end of 

each chapter (starting in Chapter 3.0), a summary of 

applicable best practices is presented for the targeted 

audience, which includes sensor manufacturers and water utilities. 

Water Utilities and

Sensor Manufacturers

• Online water quality monitoring alone will not 

provide a holistic CWS.

• Integration of data streams such as consumer 

complaint surveillance, enhanced security 

monitoring, public health surveillance, and 

triggered sampling and analysis with the online 

water quality monitoring is necessary for realizing 

the full benefits from a CWS.

• EPA has developed several guidance and guideline 

documents, a modular response protocol toolbox, 

and other software tools for utilities planning to 

establish a comprehensive CWS. The relevant 

software tools include TEVA-SPOT for locating 

online sensors and CANARY event detection 

software. The bibliography section includes a 

listing of the related EPA documents.

• Manufacturers should design flexibility into the 

sensor equipment to output real-time data streams 

in a variety of formats, which allows for analysis 

by both external and/or internal event detection 

algorithms.

• In addition to helping achieve regulatory 

compliance (e.g., monitoring residual disinfectant 

levels), sustainable online CWS equipment 

can provide other benefits that can lead to 

improvements in: distribution system water quality; 

treatment process control; distribution system 

control; customer service; and overall security.
2-1

2.0	 Online	Detection	

Equipment	and	Testing

The focus of this research was to identify water quality parameters and online sensor technologies that

could be used to detect anomalous changes in water

quality due to contamination event(s) within a water distribution system. The sections of this chapter

briefly describe the following: testing apparatus, contaminants and injected concentrations, disinfectants,

water quality parameters and online instrumentation,

data collection and analysis, event detection, and field

applications.

2.1	Description	of	Testing	

Apparatus

The first round of testing for online water quality sensor instrumentation was conducted using recirculating DSS Loop No. 6 located at the T&E Facility in

Cincinnati, Ohio. DSS Loop No. 6 was essentially

operated as a closed system during the sensor testing

period. At the conclusion of the first round of tests,

some of the research stakeholders expressed concern

that the recirculation mode operation of DSS Loop

No. 6 enhanced the detection ability of the sensors.

In this mode, the contaminant is recirculated within

the distribution system, thereby allowing the sensor

to detect the same slug of contaminant multiple times.

Subsequently, later rounds of testing involved the use

of the Single Pass DSS, also located at the T&E Facility in Cincinnati, Ohio. 

Concurrent to the DSS Loop No. 6 and Single Pass 

DSS testing, EPA conducted a series of bench-scale 

minimum dosing tests. In these tests, the selected contaminants in a water matrix (at various concentrations) 

were exposed to the online sensors to establish the 

minimum dosage/concentration of the contaminant 

where a “response” to various water quality parameters was produced by the sensor instrumentation.

2.1.1	Recirculating	DSS	Loop	No.	6	

Recirculating DSS Loop No. 6 consists of a 15-year 

old, 6-inch-diameter unlined ductile iron pipe and is 

one of six pipe loops within the DSS (Loop Nos. 1 

through 6). DSS Loop No. 6 is approximately 75 feet 

long and has a total capacity of approximately 150 

gallons. DSS Loop No. 6 is equipped with a 3-horsepower pump capable of circulating water through the 

loop at a rate of up to 110 gallons per minute (gpm). 

The loop is normally operated at a flow rate of 88 gpm, 

which produces a velocity of 1 foot per second (ft/sec) 

in the main pipe. The process flow schematic of the 

DSS Loop No. 6 used for these tests (including modifications for this research) is presented in Figure 2.1.

For the purposes of this testing, DSS Loop No. 6 was 

operated in recirculation mode using municipal tap 

water supplied by the Greater Cincinnati Water Works 

(GCWW). In this mode, the feed tanks and the 100-galFigure 2.1 Schematic of DSS Loop No. 6

Sensor loop instrumentation

Drain

Drain

Water flow

Biofilm sampling coupon

Heat

exchanger

Chlorine

injection

pump

30-gallon

feed

water

tank

Contaminant

feed tank

Recirculation

pump

(0–0.25 gpm)

Feed

water

pump

Potable

water

100-gallon

recirculation/mix

tank

Overflow

Biofilm

sampling

rack

pH

Oxidation Reduction Potential

Dissolved Oxygen

Incoming makeup water

Loop recirculation/test water

Drain

Chemical addition

Total Organic Carbon

Chlorine

Conductivity/Temperature

pH/Oxidation Reduction Potential

Turbidity

Drain

Online

Instrument Panel
2-2

lon recirculation tank are kept inline with the system. 

Operation in this mode effectively increases the volume 

of water in the system by 85 gallons, to a total of approximately 235 gallons. When operating in recirculation mode, potable water is added to the system from 

the 30-gallon feed-water tank at a rate of 0.16 gpm. At 

this rate, the entire volume of the system is exchanged 

in 24 hours. However, due to mixing in the recirculation 

tank, the time required to completely exchange the contents of the system via dilution is considerably longer. 

Injected contaminants reached the sensors in approximately 75 seconds and quickly become homogeneously 

mixed with the 250 gallons of water in the system. Dye 

tests were performed to confirm the travel time and 

mixing. The response profiles to injected contaminants 

reflect this design. An initial response after the contaminant first reaches the sensors is recorded for those sensors capable of detecting the contaminant. The response 

persist as the contaminant becomes dispersed in the 

DSS Loop No. 6, and in the sensor manifold, followed 

by a period of recovery due to dilution or consumption 

of the injected material via hydrolysis or reaction with 

free chlorine present in the tap water or through pipe 

wall reaction.

DSS Loop No. 6 is equipped with one 10-gallon chemical feed tank and a pump used to add treatment chemicals to the system. The feed tank was used to add chlorine when establishing baseline conditions prior to the 

addition of contaminants. Chlorine additions continued 

during test runs in order to keep the disinfectant levels 

stable during injections. The DSS Loop No. 6 setup also 

allowed for testing using chloramine as the disinfectant.

Two hardware modifications to the flow system of DSS 

Loop No. 6 were made to support the sensor evaluation studies. A 50-gallon feed tank with a delivery line 

to the intake side of the recirculation pump was added 

for the purpose of introducing contaminants into DSS 

Loop No. 6. Also, a sensor loop manifold (see Figure 

2.1 and Figure 2.2) was fabricated for the purpose of 

diverting water flow from the DSS Loop No. 6 to the 

online monitors under evaluation, and to collect grab 

samples for field and laboratory analyses. 

DSS Loop No. 6 was equipped with a sensor manifold incorporating the needed online sensors so that 

the studies could begin quickly. Since DSS Loop No. 

6 was operated in essentially a closed mode, the observed sensor responses were typical of a batch reactor 

operation. Essentially, the sensor response seen for the 

duration of a test run was similar to the case where 

a contaminant slug would travel through the system 

for the entire test duration (assuming minimal dispersion, mixing and general disruption of slug due to flow 

variations). The recirculation mode within the tank 

also dilutes the concentration of the contaminant in 24 

hours and does not represent a true plug flow system. 

Because there are some technically valid differences 

as compared to a “real world” distribution system, the 

recirculation mode allowed for safer contained tests, 

eliminated wastage of water, and allowed for easy 

identification of viable sensors, prior to embarking on 

studies using the Single Pass DSS as outlined in the 

next section.

2.1.2 Single Pass DSS

The Single Pass DSS was constructed of 3-inch-diameter glass-lined ductile iron pipe and spans the entire 

length (150 feet) of the T&E facility high-bay area and 

wraps back and forth across this expanse eight times. 

The combined length of this pipe is approximately 

1,200 feet and the Single Pass DSS has a total capacity of approximately 440 gallons. The pipe is gravity 

fed with tap water via a 750-gallon stainless steel tank 

mounted near the ceiling of the facility. This tank is 

supplied from a floor-mounted 1,000-gallon stainless 

steel tank. In-situ chemical feed tanks and mixers can 

be used for chlorine dosing, chemical addition, or other 

similar purpose. The contaminant injection port was installed immediately downstream of the 750-gallon feed 

tank. In addition, two sampling ports were installed at 

80-foot and 1,180-foot distances from the contaminant 

injection port. The two sampling ports supply sample 

water to multiple instrumentation racks. Figure 2.3 

shows a schematic of the Single Pass DSS within the 

T&E Facility. 

Figures 2.4 and 2.5 show the Single Pass DSS running 

the length of the T&E Facility high bay and wrapping 

its length 4 times on the east side of the pipe rack. Figure 2.6 shows the sampling ports for the inlet located 

at the top near the 80-foot mark and the outlet located 

directly below this port at the 1,180-foot distance. 

Figure 2.2 DSS Loop No. 6 Sensor Manifold and 

Instrumentation Rack
2-3

2.2	Test	Contaminants	and	Water	

Matrices

Target contaminants for the study were selected to be

representative of broad classes of biological and chemical contaminants that could be potentially introduced

into the U.S. water supply. Municipal tap water supplied by the GCWW was used as the water matrix for 

this testing. 

2.2.1	Tested	Contaminants

Table 2.1 presents a summary of the broad classes of contaminants and specific contaminants tested by EPA along 

with the associated test water matrix. The online instrumentation used to measure the individual water quality parameter responses during the testing varied due to 

various logistical reasons and the evolution of the testing 

activity during the course of the research. For example, 

most of the advanced optical instruments such as the BioSentry®, FlowCAM®, Spectro::lyserTM, and Hach FilterTrakTM 660 sc Laser Nephelometer were not procured 

prior to beginning testing that utilized the recirculating 

DSS Loop No. 6. These optical devices were purchased 

later to evaluate their efficacy in detecting biological 

contaminants. The BioSentry® and FlowCAM® instruments are designed to count and identify the injected 

biological cells. Therefore, for the purposes of evaluating 

these instruments, the following biological contaminants, 

surrogates, and growth (or carrier) media such as nutrient broths were injected into the Single Pass DSS: three 

micron beads, Escherichia coli (E. coli), E. coli (in dechlorinated water), bacteriophage male-specific (MS2), 

Bacillus globigii (B. globigii), B. globigii (in dechlorinated water), secondary effluent from wastewater treatment, 

sporulation media, sucrose, Terrific Broth, nutrient broth, 

and Trypticase soyTM broth. The biological contamination tests were performed in three distinct ways: 1) test 

cells (centrifuged to isolate the contaminant only) injected with tap water, 2) test cells in nutrient or broth solutions, 3) test cells in nutrient and broth solutions preceded 

by treatment with dechlorinating agents such as sodium 

thiosulfate pentahydride and sodium thiosulfate anhydrous. The last test was performed because real-world 

contamination events might be conducted in conjunction 

with dechlorination in an attempt to make the cells more 

750-gallon

holding tank Overflow

to Drain

Contaminant

feed

container

Chemical

injection

pump

Feed

water

pump

1000-gallon

holding tank

Online Instrument Panel

– Chlorine

– Conductivity/Temperature

– pH/Oxygen Reduction Potential

– Turbidity

– Dissolved Oxygen Sample Port 1

80 ft

Online Instrument Panel

– Chlorine

– Conductivity/Temperature

– pH/Oxygen Reduction Potential

– Turbidity

– Dissolved Oxygen

Sample Port 2

1,180 ft Drain

Tap water

feed

Figure 2.3 Schematic of Single Pass DSS

Figure 2.4 Single Pass DSS – Longitudinal View

Figure 2.5 Single Pass DSS – Connecting Pipe Elbows

Figure 2.6 Single Pass DSS – Sampling Ports
2-4

viable. The presence of free chlorine at the typical residual levels [~1 milligrams per liter (mg/L)] is deleterious to 

many biological organisms and reduces the efficacy of a 

biological attack. The bacteriophage MS2 tests were performed to simulate a viral threat. To evaluate the impact 

of nutrient broth and the dechlorinating agents, the following “control” injections were also performed: sodium 

thiosulfate pentahydride, sodium thiosulfate anhydrous, 

sucrose, terrific broth, and nutrient broth. 

2.2.2 Test Water Matrix 

The GCWW water supply to the T&E Facility comes 

from the Miller Plant, which treats water from the Ohio 

River. GCWW uses chlorine as the residual disinfectant 

for water distribution. The background range of values 

for the routinely measured water quality parameters at 

the T&E Facility are as follows: free chlorine – 0.8 to 

1.1 mg/L, specific conductance – 300 to 600 microsiemens per centimeter (μS/cm), oxidation reduction potential (ORP) – 500 to 700 millivolts (mV), potential of 

hydrogen in standard units (pH) – 8.5 to 8.8, turbidity 

< 0.1 nephelometric turbidity units (NTU), and total organic carbon (TOC) – 0.3 to 1.3 mg/L. Only the free 

chlorine levels were adjusted as needed (prior to testing) such that the levels were approximately 1 mg/L.

The chloraminated water was prepared in batches using a 2,400-gallon tank. GCWW-supplied tap water 

was collected in a 2,400-gallon tank at the EPA T&E 

Facility and tested for total chlorine residual. Calculations were made to determine the correct amount of 

sodium hypochlorite necessary to raise the total chlorine concentration to the desired level, usually 2 mg/L. 

When this concentration was achieved and verified by 

analysis, ammonium hydroxide was added in sufficient 

quantity (chlorine to ammonia ratio of 4:1) to convert 

the free chlorine into combined chlorine. The resulting 

chloraminated water was mixed for 15 to 20 minutes 

and retested for both free and total chlorine.

2.3 Water Quality Measurement

Prior to introduction of contaminants, water-quality 

sensors located within the selected test apparatus (i.e., 

DSS Loop No. 6 or Single Pass DSS) were typically 

monitored for an hour to establish normal (baseline) 

conditions. After contaminant injection, data from the 

various sensors were monitored and recorded. The sensor data were supported by the analysis of grab samples 

taken from the test apparatus at discrete intervals. For 

experimental control, uncontaminated test water matrix 

was injected into the test apparatus. During the testing, 

it was verified that the act of injection did not affect 

baseline conditions as characterized by sensor response. 

2.3.1 Measured Water Quality Parameters

A variety of water quality parameters was measured 

during the testing period. The specific instrumentation used in individual test runs for both DSS Loop 

No. 6 and the Single Pass DSS was dependent on the 

availability of instrumentation during the testing period. Table 2.2 presents an overall summary of the 

Contaminant 

Class

Specific

Contaminant

Recirculating 

Loop

Single 

Pass

Cl2

a NH2Clb Cl2

Biologicals Bacillus globigii X

Bacteriophage MS2 X

Escherichia coli X X X

Surrogate beads X

Insecticides Aldicarb X

Nicotine X X

Real Kill®/Malathion X X X

Dichlorvos X

Phorate X X

Herbicides Roundup® /Glyphosate X X X

Dicamba X

Culture Broths Nutrient broth X

Sporulation media X

Terrific broth X

Tryptic soy broth X

Inorganics Arsenic trioxide X

Cesium chloride X

Cobalt chloride X

Lead nitrate X

Mercuric chloride X

Potassium cyanide X

Potassium ferricyanide X X

Sodium arsenite X X

Sodium thiosulfate X

Sodium fluoride X

Warfare 

Agents

Ricin Xc

G-type nerve agent Xc

V-series nerve agent Xc

Potassium cyanide Xc X

Others Blank (GAC water) X X

Secondary effluent X X X

Colchicine X

Dimethyl sulfoxide X

Dye X X

Sucrose X

Sodium fluoroacetate X

Methanol X

aChlorine

bChloramines

cTesting conducted at the U.S. Army’s Aberdeen Proving Ground’s Edgewood Chemical 

Biological Center (ECBC) Facility.

Table 2.1 Test Contaminant Matrix
2-5

Table 2.2 Measured Water Quality Parameters

Parameter Measurement Type Online Instrumentation TestedParameter Applicability

Ammonia 

– nitrogen

Continuous and grab YSI 6600, YSI 6920DW Naturally occurring form of nitrogen in the nitrogen cycle. Dissolved ammonia gas is toxic to aquatic life at concentrations 

as low as 0.2 milligram per liter (mg/L). Will be converted to 

chloramine in chlorinated drinking water.

Apparent color Grab Various laboratory instruments, Six-CenseTMVisible color resulting from turbidity and dissolved materials 

(humic material, dissolved metals, dyes, algae). Potable water 

is normally colorless after treatment.

Chloride Continuous and grab YSI 6600, YSI 6920DW Indicator of salinity. Associated with a secondary maximum 

contaminant level (MCL) of 250 mg/L in drinking water.

Conductivity 

measured 

as specific 

conductancea

Continuous and grab YSI 6600, YSI 6920DW, 

Hydrolab® DS5, Troll® 

9000, Six-CenseTM, 

Hach/GLI Model C53 

Conductivity Analyzer

Ability of water to carry an electrical current. Strong indicator of 

dissolved salts. Serves as a surrogate for total dissolved solids.

Dissolved oxygen 

(DO)

Continuous and grab YSI 6600, Hydrolab® 

DS5, Troll® 9000, SixCenseTM

Concentration of oxygen dissolved in water can serve as an 

indicator of chemical and biochemical activity in water. 

Fluorescence 

(total, humic and 

bacterial)

Continuous 

Spectrophotometric

ZAPS MP-1 Instrumental measure of fluorescence at various wavelengths.

Free chlorine Continuous and grab YSI 6920DW, Hydrolab® DS5, Troll® 9000, 

Six-CenseTM, Hach 

CL17 Free Chlorine 

Analyzer

Chlorine is added to the DSSb in the form of sodium hypochlorite. Chlorine levels in drinking water are controlled at ~1 mg/L. 

Multi-angle light 

scattering (MALS)

Continuous BioSentry® Utilizes laser-produced MALS technology to generate unique 

bio-optical signatures for classification using JMAR’s pathogen 

detection library.

Multi-spectrum (UVVis) absorptionContinuous 

Spectrophotometric

Spectro::lyserTM or 

Carb::olyserTM

UV-Vis excitation that provides a means of estimating absorption at various wavelengths. Nitrate and/or nitrite concentration, DOCc, TOC, CODd and BODe (depending on the used 

algorithm), and turbidity. Information at nearly any wavelength 

between 200 and 750 nm. 

Nitrate – nitrogen Continuous, grab, and 

spectrophotometric

YSI 6600, YSI 6920DW Essential nutrient for plants and animals. Nitrate is the most 

soluble form of nitrogen. Causes health problems in humans. 

Drinking water standard is 10 mg/L.

Oxidation-reduction 

potential (ORP)

Continuous and grab YSI 6600, YSI 6920DW, 

Hydrolab® DS5, Troll® 

9000, Six-CenseTM, 

Hach/GLI Model P53 

pH/ORP Analyzer

Indicator of dissolved oxidizing and reducing agents (metal 

salts, chlorine, sulfite ion). ORP values above 700 millivolts 

(mV) kill unwanted organisms in drinking water. A ground water 

incursion may lower ORP by increasing chlorine demand. 

Chlorination of drinking water produces an ORP background of 

~700 millivolts in GCWW water.

Particle Count Continuous Hach 2200 PCX Particle 

Counter

Counts all particles that are between 2 and 750 μm in size. 

The counted particles can be subdivided into 32 size ranges 

to identify particles of interest. For example, the particle size 

ranges could be selected to correspond to biological organisms 

such as Giardia (6-10 μm) and Cryptosporidium spp. (2-5 μm).

Particle count 

and image-based 

identification

Continuous FlowCAM® Measures particle size, count and shape. Images particles 

between 2 μm and 3 mm in size. Helps to identify and classify 

particles based on library of images.

pH Continuous and grab YSI 6600, YSI 6920DW, 

Hydrolab® DS5, Troll® 

9000, Six-CenseTM, 

Hach/GLI Model P53 

pH/ORP Analyzer

Indicator of hydrogen ion activity (acidity or alkalinity) of water. 

Most chemical and biochemical processes are pH dependent. 

Carbon dioxide/bicarbonate/ carbonate and ammonia/ammonium equilibria are pH dependent. pH of drinking water is well 

established and controlled. A change of more than 0.5 pH unit 

indicates a problem.
2-6

measured water quality parameters and a summary of 

the usefulness of each measurement in terms of water 

quality.

2.3.2 DSS Loop No. 6 Online Instrumentation

The following are online water quality monitoring 

sensor instruments that were evaluated during the various DSS Loop No. 6 test runs: YSI 6600, Hydrolab® 

DS5, Troll® 9000, Six-CENSETM, Hach Water Distribution Monitoring Panel (WDMP), and Zero Angle 

Photon Spectrometer (ZAPS) MP-1. Figure 2.2 (previously shown) and Figure 2.7 depict most of the online 

instrumentation evaluated during the DSS Loop No. 

6 testing.

2.3.3 Single Pass DSS Online Instrumentation

The following are online water quality monitoring sensor instruments that were evaluated during 

the various Single Pass DSS test runs: Hach CL17 

free chlorine analyzer; Analytical Technology, Inc. 

Model A15/62 free chlorine monitor; YSI 6920DW; 

Wallace & Tiernan® Depolox® 3 plus; Hach astroTOCTM UV process TOC analyzer; Hach WDMP; 

Sievers® RL; and Sievers® 900 On-Line TOC Analyzer. Figure 2.8 shows two Single Pass DSS instrument panels.

Figure 2.7 DSS Loop No. 6 - Online Instrumentation

Table 2.2 (continued) Measured Water Quality Parameters

Parameter Measurement Type Online Instrumentation TestedUsefulness of Parameter for Water Quality

Temperature Continuous and grab YSI 6600, YSI 6920DW, 

Hydrolab® DS5, Troll® 

9000, Six-CenseTM, 

Hach/GLI Model C53 

Conductivity Analyzer, 

Hach/GLI Model P53 

pH/ORP Analyzer

A measurement indicator of how hot or cold the water is. DO and 

specific conductance change with temperature. Biological and 

chemical activities are heavily influenced by water temperature.

Total cyanide, 

malathion, and 

glyphosate

Grab Various laboratory 

instruments

Compound-specific laboratory analysis for the purpose of determining the fate of these three contaminants in the DSS.

Total organic 

carbon (TOC)

Continuous and grab Hach astroTOCTM UV 

Process Total Organic 

Carbon Analyzer, Sievers® 900 On-Line Total 

Organic Carbon Analyzer, Spectro::lyserTM 

or Carb::olyserTM

Dissolved plus particulate organic compounds. Can range from 

0.5 to 25 mg/L in drinking water in the U.S. May be correlated to 

chemical and biological oxygen demand. 

Transmission Continuous 

Spectrophotometric

ZAPS MP-1, 

Spectro::lyserTM or 

Carb::olyserTM

Measure of color based on Beer’s Law as measured by photon 

transmission through water [800 nanometers (nm) for this study].

Turbidity Continuous and grab YSI 6600, YSI 6920DW, 

Hydrolab® DS5, Troll® 

9000, Six-CenseTM, 

1720D Turbidimeter, 

Hach FilterTrakTM 660 

sc Laser Nephelometer

Indicator of suspended matter and microscopic organisms. Pathogens are more likely to be present in highly turbid waters. 

Ultraviolet 254 

nanometer 

wavelength (UV254) 

absorption

Continuous 

Spectrophotometric

ZAPS MP-1, 

Spectro::lyserTM or 

Carb::olyserTM

Measure of organic compounds that absorb photons at 254 nm. 

Indicative of organic compounds with aromatic chemical structure 

and conjugation.

aSpecific conductance is defined as the raw solution conductivity, compensated to 77°F (25°C).

bDSS = Distribution System Simulator.

cDOC = Dissolved Organic Carbon.

dCOD = Chemical Oxygen Demand.

eBOD = Biological Oxygen Demand.
2-7

2.3.4 Single Pass DSS Online Optical 

Instruments

The following are online optical instruments that were 

evaluated during the various Single Pass DSS test runs: 

Carbo::lyserTM and Spectro::lyserTM, BioSentry®, 

FlowCAM®, Hach FilterTrakTM 660 sc Laser Nephelometer, and Hach 2200 PCX Particle Counter. Figure 2.9 depicts the instrument panel that contains the 

controller for the Carbo::lyserTM, controller for the 

Hach Filter/TrakTM 660 sc Laser Nephelometer, and the 

FlowCAM® device.

In addition to these instruments, EPA is also evaluating 

the radiation monitor (Technical Associates, Canoga 

Park, California) at the National Air and Radiation Environmental Laboratory (NAREL) in Montgomery, Alabama. The results from these tests were not available at 

the time of production of this document. Figure 2.10 depicts the radiation monitor.

2.4 Data Collection and Analysis

Data collected for each parameter from the online water quality sensor instruments were complemented by 

laboratory analyses of grab samples. To facilitate comparisons between the online monitoring results and laboratory analyses, sensor responses to contaminants for 

each parameter were plotted along with associated grab 

sample results. These plots allowed a graphic interpretation of the data to 1) evaluate changes in baseline 

conditions due to contaminant introduction, 2) compare 

sensors (using different technologies to measure the 

same parameter), and 3) recognize false negative/false 

positive responses by visual comparison to the grab 

sample data. 

2.4.1 Data Collection 

Wherever possible, each of the online sensors was 

connected to a data acquisition system. The intelligent Sensor Interface and Control (iSIC) system was 

connected to the data collection personal computer 

(PC) via hardwire or radio (as appropriate). The data 

collection PC ran the iChart software program, which 

polled the connected iSIC(s) and monitoring devices 

every 2 minutes and recorded the data reported by the 

instrumentation. The 2-minute data collection cycle 

was considered to be optimum because of the number of instruments concurrently tested that needed to 

be polled for data and the measurement cycle limitations of some tested devices. The iSIC/iChart system 

was selected as the data collection platform because 

it incorporated many pre-built device drivers that 

could communicate with the widest variety of online 

instrumentation tested at the T&E Facility. A more 

detailed discussion of the data collection system is 

presented in Chapter 4.0. Figure 2.11 shows the NexSens iSIC data acquisition system. 

2.4.2 Data Analysis 

The data plots generated from the tests conducted at 

the T&E Facility were analyzed visually to construct a 

qualitative response matrix for the contaminants testFigure 2.10 Technical Associates Radiation Monitoring 

Device

Figure 2.8 Single Pass DSS Instrument Panels

Figure 2.9 Various Single Pass DSS Optical 

Instruments
2-8

ed. The criteria for determining a “significant change” 

was subjective at the early stages of the research. The 

sensor responses were plotted over the course of the 

test runs and analyzed for visually significant changes. Thereafter, a more robust analysis was performed 

where the absolute change, percent change, and signal-to-noise (S/N) ratio for each measured parameter 

was computed. See Chapter 5.0 for further details.

At the onset of this testing effort, EPA determined 

that an automated algorithmic analysis of the online 

data was essential. Therefore, concurrent to the testing, EPA initiated collaboration with SNL for the 

development of the CANARY Algorithm (previously 

described in Chapter 1.0). In addition, EPA continues to evaluate other commercial data analysis algorithms/products as they became available (Umberg et 

al., 2009).

2.5 Teaming with EPA’s Water 

Security Initiative

The work conducted at the T&E Facility assisted 

EPA’s Water Security initiative (WSi – formerly, 

WaterSentinel). As described in Section 1.2, the 

EPA’s OGWDW-WSD worked collaboratively with 

NHSRC to deploy a pilot network of water quality 

monitoring instrumentation at GCWW as a part of 

the WSi pilot in Cincinnati (EPA, 2008c). Figures 

2.12 and 2.13 show two types of instrument panels 

deployed at the first pilot utility. The panels contain 

online instrumentation to measure free chlorine, 

TOC, pH, ORP, conductivity, temperature and turbidity. The Type A panels utilize all Hach instrumentation, whereas the Type B panels utilize instrumentation from manufacturers other than Hach. As will 

be discussed in Chapter 5.0, free chlorine and TOC 

were found to be most useful trigger parameters in 

chlorinated water systems.

Figure 2.12 First Pilot Utility - Water Security Initiative 

Instrument Panel Type A

Figure 2.13 First Pilot Utility - Water Security Initiative 

Instrument Panel Type B

2.6 EPA’s Future Water Quality 

Sensor Research 

EPA, through their Technology Testing and Evaluation Program (TTEP) and testing activities at the 

T&E Facility, will continue to identify and evaluate 

promising sensor technologies for potential use in 

CWS, as funding allows. Radiological and low density biological detection equipment testing are the key 

current sensor-related data gaps. New technologies 

are needed to reduce the current capital, operational, 

and maintenance costs in order for CWS programs to 

be sustainable. Information on sensor evaluation programs can be obtained by contacting Mr. John Hall 

via e-mail (Hall.John@epa.gov) or phone (513-487-

2814). Additional information related to EPA’s water 

protection research can be obtained at: http://www.

epa.gov/nhsrc/aboutwater.html.

WSi is a program designed to address the risk of intentional contamination of drinking water distribution 

systems. Initiated by OGWDW in response to HSPD-9, 

Figure 2.11 NexSens iSIC Data Acquisition System
2-9

Water Utilities and

Sensor Manufacturers

• Online sensors were tested in simulated distribution 

systems using chlorinated and chloraminated 

waters. The simulated systems were injected with 

a variety of target contaminants to evaluate the 

individual sensor/parameter response.

• Grab sampling is critical to verify online sensor 

responses.

• A data polling frequency of two minutes was found 

to be optimal for the wide range of sensors tested, 

but utilities might want to evaluate other polling 

frequencies.

• A robust Supervisory Control and Data Acquisition 

(SCADA) system is needed to fully utilize and 

process in real-time the large volumes of data that 

are generated.

• The EPA T&E Facility distribution system 

simulators attempt to replicate field conditions, 

but the effects of varying water demands were 

not simulated during the tests. In addition, the 

background water quality parameter levels are very 

stable at the T&E Facility. Therefore, for utilities 

with varying background water quality parameters 

compounded with varying demands, the simulated 

tests might result in different sensor/parameter 

response.

• Instrument manufacturers need to design for 

allowing an automated grab sample to be collected 

to validate the instrument response as needed.

• Instrument manufacturers should design their 

sensors so that they can be easily interfaced with a 

wide variety of SCADA systems.

the overall goal of WSi is to design and deploy CWS 

for drinking water utilities. EPA is implementing the 

WSi in three phases: (1) development of a conceptual 

design that achieves timely detection and appropriate 

response to drinking water contamination incidents; 

(2) demonstration and evaluation of the conceptual 

design in full-scale pilots at drinking water utilities; 

and (3) issuance of guidance and conduct of outreach 

activities to promote voluntary national adoption of 

effective and sustainable drinking water CWS. The 

initial full-scale pilot was implemented in Cincinnati, Ohio. EPA-OGWDW plans to implement more 

pilot studies utilizing the CWS concept presented in 

Section 1.3. These pilot studies will be conducted at 

several utilities to demonstrate that a functional CWS 

can be deployed under a variety of real-world conditions. Information on the GCWW pilot study can be 

obtained by contacting Mr. Steve Allgeier via e-mail 

(Allgeier.Steve@epa.gov) or phone 513-569-7131. 

Additional information related to EPA’s water security 

research can be obtained at: http://www.epa.gov/safewater/watersecurity/.

3-1

3.0 Instrument Setup 

and Data Acquisition

As previously mentioned in Section 2.5 (and will be discussed later in Chapter 5.0), the tests conducted at the 

EPA T&E Facility show that free chlorine and TOC are 

the most useful water quality parameters for detecting 

changes indicative of contamination in chlorinated water systems. The prototype monitoring panels installed 

by the WSi pilot utility included online instrumentation 

to measure free chlorine, TOC, pH, ORP, conductivity, 

temperature and turbidity. Each utility should evaluate 

its needs and resources (both capital and labor), and review the test results associated with its water distribution 

system before selecting a suite of online parameters and 

associated instrumentation. The use of chloramines as 

disinfectant should also be taken into account when selecting the parameters and instrumentation. Once the parameters and instruments have been selected, they should 

be set up in accordance with the instructions provided by 

the manufacturer for flow, pressure, and sample conditioning requirements. This chapter discusses in detail the 

various requirements for setting up online water quality 

sensor instrumentation at a specific site.

3.1 Site-Specific Requirements

EPA-developed software such as TEVA-SPOT should 

be used to identify the optimal locations of a fixed 

number of sensors. After a potential monitoring site has 

been identified using TEVA-SPOT, a site visit should be 

performed to ensure that the selected site has: 

• sufficient environmentally protected secure 

space for housing the selected instrumentation

• access and a clear path for transporting and 

servicing the instrumentation to conduct 

installation and maintenance activities

• adequate source of pressurized and pressurecontrolled water supply for the proposed 

instrumentation

• drainage access to discharge the water analyzed 

by the online instrumentation

• necessary power supply and backup 

(uninterrupted power supply) to power the 

online instrumentation, data collection, and data 

transmission systems

• appropriate media (wired or wireless) for 

transmitting the online data in real-time to a 

specified data collection center

• water quality characteristics that are suitable (or 

can be appropriately conditioned) for analysis 

by selected online instrumentation

Depending upon the threat and vulnerability analysis, some of the selected sites might not meet all of 

the requirements. For such sites, alternate means of 

meeting a site-specific requirement should be investigated. For example, all sites might be not suited 

for deploying a single communication technology. 

In such cases, a combination of wired and wireless 

communication technology should be investigated. 

Another example could involve a situation where 

the initial water quality is not suitable for selected 

instrumentation. In this case, either alternate instrumentation should be investigated or site/instrumentspecific sample water conditioning could be performed such as pH buffering, degassing, or removing 

iron and salts.

3.1.1 Environmentally Protected Housing

The selected site should be environmentally protected and secure. Many of the online sensors are typically contained in a National Electrical Manufacturers Association (NEMA) class 4- or 4X-compliant 

corrosion-proof enclosures and protected from windblown dust, rain, sleet and external icing. Specifically, a NEMA4-compliant enclosure has to pass the 

“Hose Test,” which is described as: a 1-inch nozzle, 

delivering 65 gpm of water, from a distance of 10 

feet, from all directions, for a 5-minute time period, 

with no water leak to the interior. Class 4X enclosures have additional protection against corrosion. 

Preferred materials for mounting (or housing) the 

online instrumentation are polyester/glass, stainless 

steel, and epoxy coatings. Although the selected enclosure might be suited for general outdoor application, there is an additional need for temperature and 

humidity control because the advanced devices are 

equipped with onboard computers and electronics 

that might not withstand the temperature, humidity, and altitude extremes. The environmental tolerances are instrument-specific and the manufacturer 

instructions should be followed to ensure the suitability of the selected housing. In general, it is not 

recommended that the instruments be housed in an 

environment where the temperature exceeds 90°F 

(32.2°C) or falls below 40°F (4.4°C). Appropriate 

cooling and/or heating devices should be installed at 

the site as needed. 

Furthermore, the selected instrumentation might have 

humidity specifications, (for example, a range of 5 to 

95%). High humidity might result in corrosion of electronic components and/or could lead to short circuits 

and malfunction. Humidity can increase the conductivity of the embedded electronics, leading to short circuits 

and malfunction. Condensation is another problem that 

can cause electronic devices to malfunction. For example, when an instrument is moved from a colder place 
3-2

to a warmer and more humid place, condensation could 

coat circuit boards and other insulators, leading to short 

circuiting inside the equipment. Such short circuits 

might cause substantial permanent damage if the equipment is powered on before the condensation has evaporated. Electronic equipment should be acclimatized for 

several hours (as specified by the manufacturer) before 

powering on. 

3.1.2 Access for Servicing the Instrumentation

The online instrumentation generally requires periodic servicing, calibration and reagent replacement. 

In a multi-instrument setup, individual instruments 

are mounted on panels that are fabricated to fit the 

space requirement and also provide easy access for 

servicing each instrument. The instruments are set 

up such that the servicing need for a single instrument does not disrupt the function of other instrumentation. Also, the water intake and drain lines 

are configured in a manner such that they are generally below the instrumentation so that any water 

line failure does not damage the instrumentation. In 

addition, the power conditioning, data logging, and 

communication equipment are separated from the 

online instrumentation. Power conditioning devices 

are designed to regulate the voltage and improve the 

power quality (e.g., electrical noise suppression and 

transient impulse protection). Figures 3.1 and 3.2 

show instrument panels that have been designed specifically to facilitate online monitoring at the EPA 

T&E Facility. For example, the sensor shown in Figures 3.1 and 3.2 has all the water lines at the bottom. 

The sample inlet line to each instrument is isolated. 

The drain lines are connected to the available floor 

drain. The data logging equipment is on the back of 

the panel and the power lines are on top.

The operator should be able to see and service the 

instrumentation/data collection components with 

ease. If the instrument panel is improperly designed, 

the operator might take shortcuts while servicing, 

which could lead to lower data quality or equipment 

malfunctions due to improper servicing.

3.1.3 Pressure-controlled Water Supply

The majority of the water quality instrumentation 

is sensitive to fluctuations in water supply pressure. 

Pressure changes can create bubbles (degassing) in 

the sampled water, resulting in erroneous data. Pressure regulator valves are used to allow water from 

a high-pressure supply line (or tank) to be reduced 

to a safer preset level specified by the instrument 

manufacturer(s). Pressure regulators are also susceptible to changes in the water supply pressure. Sometimes, it might be necessary to have multiple layers 

of pressure regulation to dampen any effects of pressure fluctuations on the instrument readings. Instruments like particle counters require separate mounted 

constant-head overflow weir mechanisms so that the 

sample outlet can be raised or lowered to the height 

that will produce the desired flow. Figure 3.3 shows 

the constant head mechanism for a particle counting 

device.

By pushing water up a fixed-height column and collecting the sample stream from that column, a constant 

Figure 3.1 Single Pass DSS Instrument Panel at 80-foot 

Sampling Location

Figure 3.2 Single Pass DSS Instrument Panel at 1,180-

foot Sampling Location
3-3

pressure is delivered to the instrument. This method 

of regulated sample delivery, although simple, is very 

effective in controlling pressure and flow fluctuations. 

Hach uses this method of sample delivery for Hach 

CL-17 free (or total) chlorine analyzers and Hach 2200 

PCX Particle Counters. The BioSentry® unit also employs a similar method for delivering constant sample 

flow.

3.1.4 Drainage Access

The sampled water drawn from the online instrumentation panel needs to be discharged appropriately to 

meet local discharge requirements. Generally, access to 

a sanitary sewer line is sufficient. In certain locations, 

such access might not be easy. Care should be taken 

so that water does not pool near the instrumentation, 

causing a slipping hazard. A drain manifold is recommended for locations with multiple online instruments. 

The drain line should be sized adequately, taking into 

account any instrument and inlet line failures.

3.1.5 Power Supply and Electrical Protection

Adequate power supply (preferably 3-phase) with a 

backup device for uninterrupted power supply (UPS) 

intended to provide sufficient power for the online instrumentation, data collection, and data transmission 

systems is recommended. In addition, the electrical circuits to each instrument should be isolated via a circuit 

breaker, and both the panel and instruments should be 

appropriately grounded. The electrical isolation allows 

for servicing of individual instrumentation without disrupting the other equipment installed at the location. 

Circuit breakers protect the instrumentation from electrical surges and short circuits. Connection to ground 

is a safety issue designed to protect the personnel servicing the instrumentation and the online instrumentation. The ground connection also helps limit build-up of 

static electricity on the instrumentation. In areas where 

the line voltage is known to fluctuate, a surge protector 

is also recommended. The surge protector regulates the 

voltage supplied to the instrument by either blocking or 

by shorting to ground connection when voltages above 

safe instrumentation thresholds are sensed in the circuit.

A UPS or an inline battery backup lasting four to eight 

hours is recommended, because it continuously powers and protects the instrumentation from the previously described power problems. A UPS is also known 

as a power or line conditioner because of this ability. A 

UPS generally contains a lead-acid battery for storing 

power. During electrical outages, the energy reserves 

stored in the UPS are used to power the instrumentation. Figure 3.4 shows a field data communications 

NEMA 4 enclosure with backup UPS Power. Figure 3.3 Example Constant Head Mechanism for 

Hach 2200 PCX Particle Counter.

Figure 3.4 Field Communications Enclosure.
3-4

Some instruments provide the option for portable or 

line power. For example, the YSI 6920DW instrument 

can be powered by battery or line power. Line power 

is preferred in installations where the line is backed up 

with an appropriately sized UPS.

3.1.6 Transmission Media Access

In order to fully realize the benefits of online instrumentation, appropriate media (wired or wireless) 

should be used in real-time for transmitting the data or 

information to a pre-specified data collection location. 

Wired media generally provide higher bandwidth, but 

could be cost-prohibitive in certain locations. In these 

cases, the use of wireless media (e.g., licensed or unlicensed radio, cellular, satellite-based transmission 

media options) should be investigated. In some cases, 

depending upon the location and media used, the data 

transmission might be susceptible to various types of 

interferences. In such cases, additional programmatic 

error control techniques should be applied to mitigate 

the errors during transmission. For example, in a pollbased data collection platform, it might be necessary 

to either increase the number of retries on a failed 

poll event or adjust the data packet reception window 

based on the bandwidth and latency limitations of the 

selected media.

3.1.7 Source Water Quality Adjustment

Generally, the selected instruments need to be suitable 

to analyze the source water quality. In some cases, the 

source water quality can be adjusted to meet the instrument specifications. For example, certain free chlorine 

measuring devices require the pH of the water to be 

below 8.5 standard units. If the pH at the selected location is above 8.5, appropriate buffering agents (e.g., 

carbon dioxide) should be used to condition the pH of 

the sampled water or an alternate online monitoring 

instrument should be selected for that parameter. For 

example, the Sievers® RL unit is not appropriate for 

high pH water (> 8.5).

3.1.8 Instrument-Specific Accessories

As discussed in the previous section, for some instruments, there might be a need for either peripheral 

support equipment (or accessories) that precondition 

the sample water or for carrier gases to complete the 

analysis. The Hach TOC monitor is another example of an instrument that requires specific accessory 

equipment as identified below.

In general, TOC monitors are one of the more complex instruments to operate. Hach uses the ultraviolet (UV) persulfate method; this method requires 

reagents (sodium persulfate and phosphoric acid) to 

drive the oxidation reaction. These reagents are supplied in 5-gallon carboys and are bulky to handle. 

This instrument also requires a clean, carbon dioxide (CO2)-free air source to carry sample flow to the 

CO2 detector. The CO2-free air source is supplied 

either by a cylinder of liquid nitrogen, or a zero air 

generator. If a zero air generator is used, an air compressor is needed to supply a constant stream of air. 

A considerable amount of space is required to house 

this monitor and its supporting equipment. These 

units have proven to be fairly labor-intensive to operate and require a highly skilled technician to perform maintenance and calibration procedures. Figure 3.5 shows a Hach astroTOCTM UV process TOC 

analyzer instrument and associated zero air system.

The Sievers® 900 On-Line TOC Analyzer also uses 

the UV persulfate method. Similar to the Hach unit, 

the Sievers® 900 On-Line TOC Analyzer can be 

fairly labor-intensive to operate and requires a highly 

skilled technician to perform maintenance and calibration procedures. However, this instrument and its 

reagent packs are more compact than the Hach unit. 

Also, the Sievers® 900 On-Line TOC Analyzer does 

not require an external zero air system/compressor 

or a liquid nitrogen Dewar. This unit does require 

an inorganic carbon remover (ICR) for waters that 

are heavily laden with inorganic carbon. Figure 3.6 

shows a Sievers® 900 On-Line TOC Analyzer (the 

ICR is contained inside the instrument enclosure).

Unless the utility has extensive in-house experience with these instruments, it might be prudent to 

procure service contracts for each of the aforementioned TOC units. Surrogate TOC monitoring equipment using UV-visible (UV-Vis) spectral absorbance 

has been found to be less labor intensive, but tradeoffs in its limited ability to detect a variety of potential organic contaminants should be taken into 

consideration.

Figure 3.5 Hach astroTOCTM UV Process Total Organic 

Carbon Analyzer.
3-5

3.2	Calibration	Materials/Reagents	

and	Onsite	Accessories

During the setup, instrument calibration material, 

reagents and accessories ought to be available (as 

needed) to ensure that the instruments are operating 

as recommended by the manufacturer. Many reagents 

and calibration solutions have expiration dates; therefore, these reagents should be ordered according to the 

instrument-specific maintenance schedule. While performing calibration and maintenance activities, each 

manufacturer’s procedure needs to be followed to ensure that the instrument is performing properly and is 

measuring the water quality in the designated range. 

Also, proper calibration ensures that the quality of the 

data is reliable.

3.3	Data	Acquisition	System

Most water utilities implementing a network of online 

instrumentation generally have some type of SCADA 

system. SCADA systems are also known as industrial 

control systems and are capable of monitoring and controlling a process. Generally, water treatment plants are 

automated with some type of SCADA system. From a 

water utility perspective, a SCADA system generally 

consists of the following four components: 

1. a Human-Machine Interface (HMI), which is a 

combination of computer software and hardware 

that presents information to an operator; the 

operator is able to monitor and control the 

process/instrumentation through this interface.

2. a supervisory or a central node that gathers data 

from a programmable logic controller (PLC) 

and/or a Remote Terminal Unit (RTU) for 

presentation to the operator through the HMI 

and sends commands to the PLC/RTU based on 

the operator inputs from the HMI.

3. PLCs/RTUs connected to the online 

instrumentation that convert sensor signals to 

digital data (inputs) and send commands to 

connected automated devices (such as sampling 

devices and pumps) to perform a pre-defined 

task based on operator commands from the 

HMI.

4. a data communication infrastructure connecting 

the supervisory system to the PLC/RTU.

Figure 3.7 shows a data flow schematic from field-deployed online instrumentation to the operator in a control center.

Historically, SCADA system hardware and software 

tend to be proprietary. Water utilities that have invested 

in a particular manufacturer’s solution might find themselves restricted to limited choices for equipment when 

considering system expansions or upgrades. However, 

most SCADA systems can communicate with sensors 

or instrumentation that can provide their data output in 

4 to 20 milliamperes (mA), or through serial protocols 

such as Recommended Standard 232 (RS-232)/Recommended Standard 485 (RS-485). The RS-232/RS-485 

electrical specifications are defined by the Electronic 

Industries Alliance (EIA) for a serial communications 

channel. 

Remote

PLC

RTU Controller

Comms

Protocols

Ethernet

Serial

Wireless

I/O

Meters

Sensors

Field Devices

Master

SCADA Server HMI

Field

Devices

Control

Center

Figure 3.7 SCADA Data Flow Schematic

Figure 3.6 Sievers® 900 On-Line Total Organic Carbon 

Analyzer.

3.3.1	4	to	20	Milliamperes	Current	Output

Developed in the 1950s, the 4 to 20 mA instrument outputs are still widely used by SCADA and instrument 

manufacturers. This output format is ideally suited for 

low-cost instruments that provide one or two analog 
3-6

output values. Generally, the output signal can travel 

distances of around 50 meters. In other words, the PLC/

RTU capturing this data output from the instrument 

must be located within 50 meters of this instrument. 

This output is easy to understand and troubleshoot: a 

signal of 4 mA represents zero percent of the output 

span and 20 mA represents one hundred percent signal 

output span. For example, a chlorine monitor calibrated 

to measure a span between 0 and 5 parts per million 

(ppm) will provide a corresponding analog output between 4 and 20 mA when reporting these values. Troubleshooting the output is simple, requiring only a digital voltmeter to read the values inline.

3.3.2 Serial Protocols

Developed in the 1960s, the RS-232 is a serial protocol 

for sending and receiving signals between a Data Terminal Equipment (DTE) and Data Circuit-terminating 

Equipment (DCE). Prior to the popularization of the 

Universal Serial Bus (USB), the RS-232 serial port was 

commonly available with all types of personal computers. The RS-232 connection (at a minimum) needs 

3 wires to communicate where one wire is dedicated 

to transmitting data, one to receiving data, and one is 

ground. The RS-232 can even use a two-wire connection (data and ground) if the data flow occurs one way. 

The RS-232 standard defines the voltage levels that 

correspond to logical one and logical zero levels. Valid 

signals are plus or minus 3 to 15 volts. 

RS-485 (also known as EIA-485) is a multipoint serial 

communications channel that can span distances of up 

to 4,000 feet. The multipoint communication is often 

in a master-slave arrangement when one device dubbed 

“the master” initiates all communication activity with 

other devices in the network. RS-485 is used as the 

underlying protocol in many standard and proprietary 

SCADA protocols, including the most common versions of Modbus.

3.3.3 Data Communication Protocols

Typical legacy SCADA communications protocols include Modbus (developed by Modicon), RTU Protocol 

(RP-570) and PROFIBUS. These communication protocols are all proprietary and SCADA-manufacturer 

specific, but are widely adopted and used. During the 

late 1990s, many of the SCADA manufacturers shifted 

toward more open communication protocols and adopted the “de facto” open message structure offered by 

Modbus over serial communications protocols such as 

RS-232/RS-485. Since 2000, most SCADA manufacturers are offering greater open interfacing operability 

by adopting standards such as Modbus Transmission 

Control Protocol (TCP) over Ethernet and Internet Protocol (IP). Other standard communications protocols 

include: International Electrotechnical Commission 

(IEC) 60870-5-101 or 104, IEC 61850 and Distributed 

Network Protocol 3. These protocols are standardized 

and recognized by all of the major SCADA manufacturers. Similar to Modbus, many of these protocols now 

contain extensions to operate over TCP/IP.

3.3.4 SCADA Setup and Poll Rate

Generally speaking, most water utilities embarking on 

online monitoring have some type of SCADA system in 

place. It is generally cost-effective to expand on existing 

SCADA systems to accommodate online water quality 

monitoring and integrate it with distribution system and 

treatment plant operations as needed. For a large utility 

(serving >100,000 persons), it is common to have tens 

of thousands of SCADA tags [or SCADA input and output (I/O) values] that are polled by the SCADA “master device” periodically. Depending upon the poll cycle 

and available data bandwidth, polling most of these 

SCADA I/O values every one to five minutes is common. The online water quality instruments themselves 

have a sampling cycle, and a vast majority of these have 

sampling and reporting cycles of less than one minute. 

However, in some cases, the sample cycles for achieving peak measured values might be between four and 

eight minutes (e.g., Hach astroTOCTM UV process TOC 

analyzer and Sievers® 900 On-Line TOC Analyzer). 

The data acquisition system used at the EPA T&E Facility was set to poll every two minutes. Based on a review 

of the data generated during this testing, the researchers 

at the T&E Facility conclude that a device poll rate of 

every two minutes is sufficient to produce data quality 

that can reliably be processed by algorithms to evaluate 

significant changes in water quality that is protective of 

human health for most locations. However, the utilities 

might want to evaluate other polling frequencies.

3.3.5 Data Marking

The SCADA system should be set up so that calibration events, bad data, and instrument warnings (low reagent) are captured and “marked” within the SCADA 

water quality database. This will permit the algorithms 

analyzing the data in real-time to exclude these marked 

data from further analysis, as any anomalies resulting 

from these data are unlikely to be actionable.

3.3.6 Data Transmission and Storage

Data transmission at the T&E Facility and nearby associated locations use a variety of communication media, including wired and wireless (radio and cellular) 

technologies. 

For large SCADA implementations, the majority of the 

newer SCADA software manufacturers recommend 

the use of a centralized (or distributed) database as the 

back-end data repository. Generally, older data that are 

not needed for any real-time analysis or computations 
3-7

Figure 3.8 T&E Facility NexSens iSIC Datalogger

are archived/stored in a database traditionally referred 

to as the “historian.” The real-time and the near realtime values are usually run through an event-driven 

calculation engine that either automatically performs a 

task when predefined conditions are met or raises an 

alarm for the operator to intervene or acknowledge the 

SCADA value exception. Typically, the commercial 

SCADA systems only allow the operator to define set 

points (both at high and low levels) for each monitored 

parameter to trigger an alarm.

The database storage and retrieval mechanism allows 

for add on algorithm-type programs, which can evaluate the water quality data in real-time, compare it to the 

baseline data in the database, and raise alerts and alarms 

based on computed values and data trends.

The T&E Facility water quality data is stored at multiple locations. The iSIC datalogger and the iChart 

application store the data in proprietary formats. The 

iChart stores the data in encrypted eXtensible Markup 

Language (XML) and also pushes the data to a separate Open Database Connectivity (ODBC)-compliant 

MySQL database for storage and retrieval over the network using commonly available tools. Figure 3.8 shows 

the T&E Facility NexSens iSIC datalogger.

3.4 Best Practices for Instrument 

Setup and Data Acquisition

Each utility, equipment developer/manufacturer should 

review the site-specific requirements identified in Section 3.1 of this document. For the utilities, if technically feasible, a single standard type of panel mount for 

housing all of the instrumentation/ SCADA is recommended. In cases where a one-size-fits-all solution is 

not possible (due to space constraints), no more than 

two or three types of standard panel designs are recommended for field implementation. Each of the additional panel types could be designed to eliminate sitespecific length, width or depth constraint(s). The panel 

standardization makes the fabrication and maintenance 

easier. Within each type of panel, the following design 

factors are of prime importance:

• Each instrument should be both electrically and 

hydraulically isolated (i.e., each instrument has 

its own circuit breaker, separate water inlet with 

a ball valve).

• Flow monitoring devices should be nonfouling (i.e., a rotameter without flow control 

or float guide-wire, which tends to accumulate 

biological growth and particle debris) and 

instrument-specific in the correct flow range.

• To conserve water, some of the instruments can 

be designed to accept the discharge of another 

non-reagent based instrument.

• In cases where the inlet water tends to be colder 

than the environmental housing, degassing 

(bubbles) can negatively impact the performance 

of some instruments. The discharge side can be 

pressurized for some instruments to minimize 

the degassing effect. In other cases, a bubble 

trap or a constant head mechanism could prove 

effective.

• The panels should be accessible and well-lit 

(with an external light source)

• The sites should have sufficient space to be 

ergonomically efficient. This will prevent the 

operator from taking shortcuts while performing 

maintenance activities.

• There should be a workbench, restrooms, a 

place to store supplies and chemicals onsite to 

maximize operator efficiency.

Equipment manufacturers should try to minimize the 

footprint of their device and ensure that the housing is 

NEMA-compliant. Wherever possible, the fluid lines 

should include moisture sensors and be below the electrical and data acquisition components to minimize 

damage in case of a leak. In case of malfunctions, the 

instruments should be robust and have an alarm function and self-restarting capability.

Data acquisition using field RTUs should be standardized by the water utility so that the programming can 

be simplified and replicated across sites. If data transmission at a particular location is prone to interferences, programmatic error control techniques should 

be applied to mitigate the errors. The data acquisition and communication units should be UPS-backed 

and equipped with lightning/surge protection. The 
3-8

manufacturers should ensure that the sensor is able to 

communicate with the field RTUs, using the common 

SCADA communication protocols.

Wireless data transmission should use secure protocols 

where possible. Other SCADA related cyber-security 

recommendations should be implemented whenever 

possible. The data should be stored in databases with 

ODBC connectivity and routinely backed up. The 

ODBC connectivity enables the use of third party data 

analysis tools/applications or event detection algorithms such as CANARY to interface with the data in 

real-time. In addition, data should be marked for instrument alarms, errors, and calibration events so that they 

can be filtered out by the algorithms while analyzing 

the data for anomalies.

Water Utilities and

Sensor Manufacturers

• Trade-offs should be considered when locating 

online sensors at the optimal TEVA-SPOT 

identified location or at an alternate nearby 

utility-owned location that meets site-specific 

requirements identified in Section 3.1.

• Pressure fluctuations, flow control, bubble 

formation, and higher pH values might impact data 

quality of many online sensors. Manufacturers 

should provide robust non-fouling flow controls 

with the sensor and eliminate the potential for 

bubble formation in their equipment. Both utilities 

and manufacturers should consider the addition of 

pressure regulators and constant head devices prior 

to sensor elements.

• Manufacturers should add alarm output channels to 

identify instrument-related problems such as low 

reagents, instrument calibration drifts, etc. Also, 

manufacturers should provide a variety of interface 

options for SCADA communications protocols. 

In addition, the instruments should be designed to 

have a small footprint with built-in self-restarting 

capability in case of malfunctions.

• Utilities considering setting up a panel of 

instruments should review the important panel 

design factors identified in Section 3.4. Utilities 

should also standardize the data acquisition 

approach and follow the best practices identified in 

Section 3.4.

• Online TOC monitoring equipment employing 

UV-persulfate methods are expensive and 

difficult to maintain. Factory service contracts are 

recommended. One of the TOC instruments, as 

tested at the T&E Facility, requires a carbon-free 

air source (i.e., a compressor/generator or nitrogen 

tanks). Manufacturers should design and fabricate 

simplified TOC monitoring devices.
4-1

4.0 Testing Procedures 

and Safety Precautions

Prior to evaluating various online sensors at the T&E 

Facility, a Quality Assurance Project Plan (QAPP) and 

a Health and Safety Plan (HASP) were developed. The 

QAPP outlined the experimental and analytical objectives. The HASP outlined the safety precautions necessary for handling the selected contaminants. Some 

of the critical elements obtained from the QAPP and 

HASP that are applicable to the testing program (which 

might benefit water utilities and manufacturers of sensors considering such internal testing) are described in 

the following sections.

4.1 Blank/Control Injection

Prior to injecting any contaminant into the DSS (Single 

Pass or Loop No. 6), a control run was made to ensure 

that there were no significant contributions to the baseline water quality sensor response from the pumping action of the injection apparatus, the DSS itself, or any 

associated instrumentation. The blank/control injection 

matrix was designed to match the water matrix for each 

specific contaminant and was either Cincinnati tap water 

or granular activated carbon-filtered Cincinnati tap water. This procedure ensures that significant changes are 

not caused by either the absence of the selected disinfectant (chlorine, chloramines) or naturally occurring material in the injected water. Figure 4.1 shows the injection 

apparatus used for testing on the Single Pass DSS.

4.2 Contaminant Injection 

Procedures

As discussed previously in Section 2.2, various contaminants, surrogates, carrier/growth media were selected 

to represent a range of chemical, biological and radiological agents that might be accidentally or intentionally introduced into a water distribution system. This 

section provides details on the injection specifics.

4.2.1 Concentration of the Injected 

Contaminant

Several factors were considered while establishing the 

injected concentration/dosage of the selected contaminants. These included the following: solubility of the 

selected contaminant in water, results from the benchscale minimum dose sensor response study, and target 

concentrations lower than the Immediately Dangerous 

to Life or Health (IDLH) level for the selected contaminant. Mixing times and solubility observations were 

made from beaker tests before performing the injection 

event. The bench-scale minimum dose sensor response 

study was performed to determine the “detection limit” 

associated with a particular water quality monitoring 

sensor for the selected contaminant. The purpose of the 

bench-scale and the DSS Loop No. 6 and Single Pass 

DSS studies was to determine if it was possible to inject 

a contaminant at a concentration that was high enough 

to cause health effects, but could not be detected by the 

array of sensors. Contaminant concentrations of 1 mg/L 

were typical for both the DSS Loop No. 6 and the Single Pass DSS injections. This concentration was usually detectable by at least one water quality sensor; yet, 

for a vast majority of the contaminants, it represented a 

concentration well below the IDLH level. In comparison, other EPA-sponsored Environmental Technology 

Verification (ETV) studies have been conducted using contaminant injection concentrations of 10 mg/L, 

which are generally well within the detection range of 

most instruments and suitable for tracking the precision 

and accuracy of the test instruments.

4.2.2 Duration of Injection

For the purposes of determining the minimum duration 

necessary to detect a water quality baseline change, 

2-minute injections were performed. These shortduration injections were successfully detected by the 

sensors, even though some of the instrument sampling 

durations exceed the 2-minute injection period. To evaluate total dosage necessary to cause potential harm to 

humans, a longer 20-minute injection duration was selected. This duration also allowed for stable tests with 

a maximum response time long enough to see a change 

in baseline that could be detected by automated algorithms. After injection, data from the various sensors 

were monitored and recorded for at least 4 hours for 

the DSS Loop No. 6 tests, and for at least 1 hour for the 

Single Pass DSS tests. The algorithms and data analysis 

techniques are discussed in Chapter 5.

4.2.3 Water Main Flow Rate and Injection Rate

The flow rate through DSS Loop No. 6 was typically 

kept at 88 gpm, which also translates to a velocity of 

Figure 4.1 Injection Apparatus for the Single Pass DSS
4-2

1 foot per second (ft/sec) through the 6-inch pipe. This 

velocity is commonly encountered in a distribution 

system. Similarly, most of the testing that was conducted on the Single Pass DSS was performed at a flow 

rate of 22 gpm, which also yielded a velocity of approximately 1 ft/sec in the 3-inch pipe. All of the testing was conducted under turbulent flow conditions. In 

some of the tests, the flow rate of the Single Pass DSS 

was varied to obtain the desired contaminant dilution 

effect based on available stock concentrations. The following flow rates were used for testing in the Single 

Pass DSS: 5, 10.7, 22, and 40 gpm. The typical injection rate was 0.5 liter per minute (0.13 gpm). Whenever technically possible (based on solubility and any 

other constraints discussed in Section 4.2.1), a batch of 

10 liters of contaminant in water was injected over a 

20-minute period.

4.2.4 Neat Compounds Versus Commercial 

Off-the-Shelf Products

For the purposes of evaluating how effective the online 

sensor instrumentation is in detecting herbicides and 

pesticides, manufacturers of some of the commercially 

available off-the-shelf products were contacted to obtain the neat (or pure) form of the active ingredient 

in the product. During the bench-scale studies, it was 

discovered that the inactive ingredient in commercial 

off-the-shelf herbicides/pesticides might change water 

quality in a detectable manner. For the DSS Loop No. 

6 testing, Real Kill® (pesticide) and Roundup® (herbicide) were used to represent large groups of similar 

commercially available compounds that are readily 

available and accessible.

4.2.5 Wastewater and Ground Water Injections

Wastewater and ground water injections were conducted to simulate natural or accidental contamination 

events such as cross-connections and broken mains. 

There have been cross-connection and back flow 

events reported where contaminated wastewater has 

entered the distribution system. Also, it is possible for 

mains under the water table to seep or infiltrate ground 

water. 

4.3 Testing and Analytical 

Confirmation

In addition to the blank and control injections described 

in the previous section, the DSS tests were repeated 

both for DSS Loop No. 6 (in triplicate) and Single Pass 

DSS (in duplicate) to ensure that the sensor responses 

were valid and repeatable. The EPA ETV studies conducted at the T&E Facility also evaluated inter-unit reproducibility by deploying multiple units concurrently 

for testing purposes. In addition, NHSRC’s TTEP is 

designed to provide reliable information regarding the 

performance of homeland security related technologies. More information on the TTEP program can be 

obtained from the EPA website: http://www.epa.gov/

nhsrc/ttep.html.

4.3.1 Testing Confirmation

The initial rounds of triplicate testing in DSS Loop 

No. 6 yielded consistent results based on the direction 

of parameter-specific change. For the later rounds of 

testing, only duplicate runs were performed. In case a 

test run yielded inconsistent results due to equipment 

malfunction, an additional test run was performed as 

needed. Figure 4.2 shows a sample graph of triplicate 

test results for glyphosate conducted on DSS Loop No. 

6. Figure 4.3 shows a sample instrument response with 

increasing injected contaminant (glyphosate) concentrations, conducted in the Single Pass DSS.

4.3.2 Analytical Confirmation

Bench-top analytical tests were performed to confirm 

the water quality parameter readings of the online instrumentation. As shown in Figure 4.2, the grab samples 

matched the results of the online instrumentation, with 

the exception of ORP. The ORP readings are altered 

when the sample is exposed to atmosphere during the 

grab sampling event. In addition, for some of the contaminants (malathion and glyphosate), to ensure that 

the injected contaminant was not absorbed/adsorbed 

into the biofilm or pipe material, grab sampling from 

the sample taps of online sensor instrumentation was 

performed. These grab samples were submitted to an 

outside laboratory to perform analytical confirmation. 

Although the analytical results confirmed the presence 

of these contaminants, the concentration levels were 

found to vary. The varied results were attributed to the 

following: 1) relatively poor analytical methods, which 

were chosen by the outside laboratory; 2) injected compounds interacted with free chlorine in the test water, 

which might have resulted in the generation of other 

by-products that were not measured; and 3) possible 

adsorption/absorption to the biofilm. However, as the 

changes in the measured water quality parameters were 

consistent with the injected level of contaminants (as 

shown in Figure 4.3), the analytical confirmation was 

abandoned to keep up with the rapid pace of testing and 

to reduce project costs.

4.4 Flushing and Baseline 

Establishment

Between the test runs, DSS Loop No. 6 was continuously operated to flush the system. In addition, prior to the 

test runs, DSS Loop No. 6 was sufficiently flushed so 

that the water quality parameters (especially turbidity 

and temperature) equilibrated and remained stable during the test. This parameter stability was confirmed by 
4-3

evaluating the online instrument baseline data to ensure 

that the instrument readings were within the “normal” 

range of operation. If the instrument readings deviated 

from normal conditions (based on operator experience), 

the instrument was recalibrated to ensure accuracy and 

repeatability.

4.5 Health and Safety Precautions

Standard laboratory personal protective equipment 

such as laboratory coats, gloves, safety glasses, and 

safety shoes were required 

during the experiments. For 

chemical contaminants, additional test/contaminantspecific protective gear might 

be required in accordance 

with the Material Safety Data 

Sheet or contaminant-specific 

HASP. 

For biological contaminants, 

depending upon the contaminant, the biohazards and the 

risk of infection should be 

minimized. All of the biological contaminants used for 

the testing at the T&E Facility were non-pathogenic. The 

surrogates closely represent 

the biological activity of real 

pathogens (Edberg et al., 2000; Lytle and Rice, 2002; 

Rice et al., 2005; Sivaganesan et al., 2006). However, as part of good laboratory practice, standard 

Biosafety Level 1 measures were implemented. Personnel had to change gloves after coming in contact 

with items that might carry biological contaminants. 

Gloves could not be placed near the face after exposure to biological contaminants. Any positive reference materials were handled with gloves in an appropriate laboratory hood. 

-0.10

0.10

0.30

0.50

0.70

0.90

1.10

1.30

-2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00

TIME (hours) 

Free Chlorine (mg/L)

Free Chlorine (Grab) DPD - colorimetric Run 1

Free Chlorine (OnLine) DPD - colorimetric Run 1

Free Chlorine (Grab) DPD - colorimetric Run 2

Free Chlorine (OnLine) DPD - colorimetric Run 2

Free Chlorine (Grab) DPD - colorimetric Run 3

Free Chlorine (OnLine) DPD - colorimetric Run 3

T-Pre Grab

T - 3 min. T - 60 min. T - 2 Hr. T - 3 Hr.

INJECTION at INJECTION + 4 HOURS

10:35

Figure 4.2 Glyphosate Triplicate Injection Run Results

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

-0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 1.20

Time (hours)

Free Chlorine (mg/L)

Test 1 - 0.4 mg/L

Test 1 Dup - 0.4 mg/L

Test 2 - 1.5 mg/L

Test 2 Dup - 1.5 mg/L

Test 3 - 3.0 mg/L

Test 3 Dup - 3.0 mg/L

Start Injection

Figure 4.3 Glyphosate Injections at Varying Concentrations
4-4

Equipment and supplies that came into contact with 

suspected biohazard materials had to be sterilized prior 

to disposal or reuse. The contaminated equipment/supplies were sterilized by either standard autoclaving or 

wiping with 0.02% bleach solution, depending on the 

extent of contamination and the type of material constituting the equipment/supplies. Waste samples had to be 

autoclaved prior to disposal. Special precautions, such 

as donning heat-resistant gloves, were required for autoclaving.

4.6 Disposal of Contaminated 

Water From Test Runs

The EPA T&E Facility operates under a discharge permit from the Metropolitan Sewer District (MSD). This 

permit authorizes the direct discharge of specified levels of contaminants to the local, publicly owned wastewater treatment facility. The aforementioned sensor 

technology testing at the T&E Facility was conducted 

so that all of the test water could be directly discharged 

to the sewer system. Utilities and sensor manufacturers 

considering such testing at their facilities should evaluate their contaminant-specific discharge limits prior to 

initiating a testing program. If the discharge limits for 

the selected contaminants are too low, it might be necessary to make alternate arrangements for disposal of 

the test water (e.g., local treatment before discharge, 

offsite shipment) or modification of the permit.

4.7 Best Practices for Testing and 

Safety Precautions

In order to avoid positive bias from any of the injection 

equipment/sampling or monitoring equipment, all testing components should be disinfected and calibrated so 

that an accurate baseline is established prior to testing.

Test contaminants (or surrogates) should be selected 

so that they represent a broad class of potential threat 

agents. The target concentrations should be at or below the levels where human health can be adversely 

affected, including considerations for sensitive/susceptible subpopulations. The selected duration of injection 

should be optimized to minimize the use of contaminants for both cost control and waste discharge considerations. Whenever possible, for instruments measuring 

physical and chemical parameters, bench-scale testing 

is recommended prior to pilot-scale or full-scale testing to determine the levels at which the selected instruments can detect the selected contaminants. 

A QAPP can help establish a detailed experimental 

plan that identifies specific types and quantities of the 

contaminant(s) involved during the testing. The QAPP 

can also help to define the overall experimental objectives, standardize the experimental procedures, establish protocols for instrument calibration (prior to testing), and establish data quality that can be technically 

defensible when reviewed.

Prior to any testing, a HASP should be developed, reviewed, and approved by appropriately trained personnel so that the tests can be performed safely. The HASP 

will identify minimum job hazards and controls, sample 

handling techniques, personal protective equipment, 

work practices and engineering controls, and spill/

emergency procedures. This documentation also helps 

in determining if the test water can be directly and safely discharged (without treatment) based on the facility’s 

existing discharge permit. Otherwise, it will be necessary to make arrangements for appropriate waste-handling procedures. In addition (if needed), HASPs can 

identify appropriate safety training programs, personal 

monitoring needs, and medical surveillance based on 

the contaminant and concentration used.

Water Utilities and

Sensor Manufacturers

• Control and blank injections should be performed 

to ensure that the water quality sensors are not 

impacted by the injection apparatus.

• The testing at the T&E Facility revealed that 

the commercially available online water quality 

sensor equipment can generate reproducible data 

responses with duplicate contaminant injections 

and also at varying concentration levels.

• Stable or predictable baseline water quality levels 

are needed to obtain useful data from the online 

water quality sensors. The variation in background 

values should be considered when locating online 

sensors. Also, baseline data should be collected for 

a sufficient time period to capture normal water 

quality variability for each location.

• Without varying water demands, contaminants were 

found to travel as a slug or in plug flow within the 

Single Pass system. Recirculating DSS Loop No. 6 

experienced fully mixed conditions within several 

minutes.

• Utilities and manufacturers considering inhouse 

testing should select contaminants that represent 

a broad class of potential threat agents, develop 

a detailed experimental plan/QAPP/HASP, 

and evaluate potential disposal options prior to 

conducting any test runs.
5-1

5.0 Data Analysis

Water quality sensor response data generated at the 

T&E Facility enabled EPA to construct a qualitative 

sensor parameter response matrix for the contaminants 

tested. Analysis of this data required that the concept of 

a significant change from baseline water quality conditions be defined. A significant change is a large enough 

deviation from normal water quality parameters that 

could be used to trigger an alarm to be transmitted to 

the data user (i.e., a drinking water utility). The criteria 

for determining a significant change in the sensor parameter was subjective at the early stages of the investigations. Initially, significant change determination was 

based on visual qualitative inspection (e.g., drop in free 

chlorine and increase in measured TOC value) of the 

plotted sensor responses over the course of the contaminant injection test runs. Later on, the significant change 

determination was based on a quantitative approach: the 

maximum change observed within a short time period 

of contact (defined as 15 minutes) of the sensor with 

the contaminant was divided by the baseline value of 

the parameter to compute a percent change or deviation 

(Hall et al., 2007). Although this method was simple 

and straightforward, it omitted several critical factors 

such as slow sensor response times and noisy background data. Therefore, the same response data was 

also evaluated using basic statistical methods that are 

described in Section 5.1 of this report. As demonstrated 

later in this chapter, the significant change threshold 

is dependent upon the variability of the baseline water 

quality data at a particular monitoring site. Based on the 

testing conducted at the T&E Facility, EPA developed 

and utilized the significant change thresholds presented 

in Table 5.1 for evaluating contaminant injection sensor response data. These threshold values are not entirely based on measured or statistically derived values; 

the operator’s understanding of the variability of water 

quality at each location should also be taken into account while developing these parameter-specific significant change thresholds. For example, the baseline water quality parameters observed at the T&E Facility are 

stable with little variance. Therefore, using these values 

at a location where the water quality baseline is highly 

variable can lead to triggering of an excessive number 

of false positive alarms. Keeping these observations in 

mind, it is recommended that the end-users employing 

this methodology should develop their own site-specific 

significant change thresholds for evaluating real-time 

water quality data.

During the course of this research, EPA was aware that 

the significant change data analysis approach, which 

used visual inspection of time series data and percent 

change from baseline, could lead to variable results 

caused by site-specific water quality differences and 

analyst bias. Therefore, a more sophisticated quantitative approach was undertaken: individual sensor responses were analyzed by computing absolute change, 

percent change, and S/N ratio. Absolute change and 

percent change analysis employ the same mathematical 

techniques described previously in the development of 

significant change thresholds. The S/N ratio analysis is 

designed to filter the level of “background noise” caused 

by frequent fluctuations in the baseline data. The S/N 

ratio is defined as the ratio of a measured value to the 

background noise. A low S/N ratio indicates that the 

change in measured value of the parameter might be 

caused by the background noise (representing routine 

fluctuations in measured baseline) rather than resulting 

from a real change in water quality due to the presence 

of contaminants (Szabo et al., 2006, 2008a, and 2008b). 

Furthermore, EPA’s field installation experience has indicated that the baseline water quality at certain locations (that are immediately influenced by utility operations) can change significantly over a short time period. 

For example, monitored parameters might fluctuate dramatically with changes in the operation of tanks, pumps, 

and valves. The monitored parameters are also affected 

by daily and seasonal changes in the source and finished 

water quality, as well as fluctuations in demand. EPA 

collaborated with SNL to build an automated algorithmic data analysis tool that combines and enhances some 

of the previously mentioned qualitative and quantitative 

approaches to distinguish between normal variations in 

water quality and changes in water quality triggered by 

the presence of contaminants (McKenna, et al., 2006). 

These types of tools are often referred to as event detection algorithms, which can read SCADA data (water 

quality signals, operations data, etc.), perform analysis 

in near real-time, and return a 0/1 result (indicating presence or absence of an alarm). All of these approaches are 

discussed further in this chapter.

Table 5.1 Parameter-Specific Significant Change 

Thresholds

Water Quality Parameter

Deviation from

Baseline Classified as

“Significant Change” 

Temperature ± 0.15°C (~0.27°F)

Specific Conductance > 5% increase

Dissolved Oxygen ± 0.2 milligrams per liter

Oxygen Reduction Potential ± 20 millivolts

Nitrate ± 10%

Chloride ± 15%

Ammonia ± 20%

Turbidity > 200% increase

Free Chlorine > 5% decrease

Total Organic Carbon > 0.1 mg/l increase
5-2

5.1 Non-Algorithmic Sensor 

Response Evaluation

The qualitative and quantitative approaches employed 

to evaluate the data generated at the T&E Facility and 

Edgewood Chemical and Biological Center (ECBC) 

are discussed in the following subsections.

5.1.1 Single Pass DSS Data Analysis

The data tabulated and reported in this section was 

generated by injecting selected contaminants (as 

listed in Tables 5.2 through 5.7) into the Single Pass 

DSS using chlorinated tap water available at the T&E 

Facility (as supplied by GCWW). As mentioned previously, the background values are generally stable 

at this location. The range of background values for 

the routinely measured water quality parameters are 

as follows: free chlorine – 0.8 to 1.1 mg/L, specific 

conductance – 300 to 600 μS/cm, ORP – 500 to 700 

mV, pH – 8.5 to 8.8 standard units, turbidity < 0.1 

NTU, and TOC – 0.3 to 1.3 mg/L. Table 5.2 shows 

the percent change of several water quality parameters for various injected contaminants in the Single 

Pass DSS (described in Section 2.1.2). The qualitative 

response information in Table 5.2 is color-coded to 

show changes that exceed 10% from the baseline value. Percent change is calculated by first calculating 

the difference between the baseline mean over one 

hour before injection, termed absolute change (AC):

AC = Speak – Sbaseline

Where, Speak = the peak sensor value between when 

the contaminant is in contact with the sensor until 

15 minutes after and Sbaseline = the mean baseline value 

for one hour immediately preceding the contaminant 

injection test. 

Percent change is then calculated as follows:

AC

% Change = Sbaseline

Table 5.2 allows for easy identification of online water quality monitoring parameters that would potentially respond to the specified injected contaminants. 

An examination of Table 5.2 reveals that free/total 

chlorine and turbidity provide a significant change 

Table 5.2 Percent Change in Sensor Parameter Response to Injected Chemical Contaminants in

Chlorinated Water – Single Pass DSS

Chemical 

Contaminants

Initial In-Pipe 

Concentration

(mg/L)Free ChlorineTotal ChlorineChloride (Cl-

)

Specific ConductanceDissolved Oxygen (DO)Oxidation Reduction Potential (ORP)pHTurbidity

Aldicarb 0.2 -9.0% -8.8% 2.3% 0.2% 0.0% -0.3% 0.3% 188.5%

1.1 -43.6% -43.5% -2.8% 0.0% -1.2% -0.6% 0.0% 487.0%

2.2 -87.6% -82.9% 2.7% 0.0% 0.1% -1.2% -0.1% -100.0%

Glyphosate 0.4 -34.4% -17.1% 3.0% -0.0% 1.2% 0.1% -0.1% 1003.5%

1.5 -77.9% -39.3% 2.0% 0.0% 1.3% -0.9% -1.4% 329.2%

3.0 -95.2% -52.4% 2.6% -0.0% 0.6% -2.6% -4.6% -100.0%

Colchicine 0.4 -2.7% 0.3% 1.6% 0.1% 3.2% 0.2% 0.3% 593.3%

1.8 -4.3% -3.9% 0.1% 0.4% 1.3% -0.4% 0.2% 157.0%

3.6 -5.6% -4.2% -0.6% 0.0% -5.0% -0.7% 0.2% 111.1%

Dicamba 0.8 -2.8% 0.0% -1.4% -0.0% -0.6% 0.6% -0.3% 0.0%

1.3 -3.1% -1.7% -1.3% -0.3% -0.5% 0.6% -0.5% -6.9%

2.6 -1.9% 0.5% -0.6% 0.2% -0.6% 0.5% -0.7% -7.1%

Dimethyl Sulfoxide 0.6 -11.8% -9.5% -1.5% 0.1% -0.4% 0.2% 0.2% 4.4%

2.0 -29.2% -26.0% -0.1% 0.1% -0.9% -0.3% 0.3% 1.0%

4.0 -46.9% -42.6% 0.1% 0.1% -1.3% -0.3% 0.1% 2.0%

Lead Nitrate 0.6 -3.9% 1.3% -5.4% 0.0% 2.3% 0.2% -0.2% 400.0%

0.7 -2.9% -2.0% -0.8% -0.1% -3.4% -0.1% -0.1% 137.2%

1.4 -0.3% -2.0% -0.7% -0.1% -0.9% -0.9% -0.1% 538.4%

Mercuric Chloride 0.4 -2.5% -1.2% -1.6% 0.0% 0.0% 0.2% -0.1% 26.3%

1.1 -1.0% -1.5% -1.7% 0.3% -7.3% 0.5% -0.7% 46.9%

2.2 -1.1% 0.1% -2.3% 0.4% -8.0% 0.8% -1.2% -1.4%

Nicotine 0.4 -14.3% -7.9% 2.4% 0.2% 1.4% 0.2% 0.3% 1200.0%

1.9 -49.3% -28.6% 0.5% 0.0% 1.5% -2.4% 0.7% 82.0%

3.8 -84.7% -47.8% 2.4% 0.1% -1.5% -4.8% 1.0% 143.4%

Potassium 

Ferricyanide

0.6 -3.7% 0.8% 11.9% -0.1% -0.4% 0.5% 0.1% 287.7%

1.6 -5.7% 8.0% 14.5% 0.5% -0.6% -0.3% -0.2% 503.2%

3.2 -4.1% 21.2% 26.3% 0.8% -0.1% -0.1% -0.1% 50.0%

Sodium Thiosulfate 

(Anhydrous)

0.2 -15.1% -13.3% 2.8% 0.4% -0.1% 0.1% 0.3% 883.3%

1.3 -75.5% -72.1% 2.2% 0.6% -1.2% -1.1% -0.1% 385.4%

2.6 -98.8% -95.1% 4.9% 0.8% -2.6% -5.3% 0.1% 645.2%

Sucrose 0.6 -2.9% -0.7% -1.0% 0.1% 1.4% 3.7% 0.0% 167.8%

1.8 -3.2% -0.4% -0.9% 0.0% -5.7% 1.1% -0.1% 449.1%

3.6 -2.6% -0.1% -0.7% 0.0% -3.8% 0.4% -0.1% 433.3%

Control Blank 0 2.4% 1.6% 5.5% 0.5% 0.5% 1.8% 0.2% 52.5%

0 1.0% 0.2% 0.6% 1.1% 1.0% 2.0% 0.1% 71.8%

AVGa 1.7% 0.9% 3.1% 0.8% 0.8% 1.9% 0.2% 62.1%

 indicates that the percent change was >10% of the baseline within 15 minutes. a Average of two control blank runs. Data presented in this table was not corrected to accommodate for the control blank response.
5-3

signal for a majority of the contaminants tested. 

Table 5.3 shows further manipulation of the same dataset (presented previously in Table 5.2). The data presented in Table 5.3 has been normalized and adjusted to 

correct for the S/N ratio for each monitored parameter. 

The S/N ratio was calculated as follows:

AC

S / N = 

sbaseline

where sbaseline = the standard deviation in the baseline 

for one hour prior to injection. 

In Table 5.3, further manipulation of the original dataset (see Table 5.2), reveals that the turbidity parameter 

is no longer a good indicator of significant change, as 

shown in Table 5.2, because the baseline signal is too 

noisy. The data in Table 5.3 also reveal that there are 

other water quality parameters that might be good at 

detecting contamination, but do not appear so at first 

glance in Table 5.2. For example, pH and ORP changed 

in response to four and seven of the eleven injected contaminants, respectively. Both parameters have stable 

baselines (low standard deviation) and produced small 

changes when contaminants were injected. However, 

because the baselines were so stable, the normalized 

and S/N-adjusted change was relatively large. Tables 

5.2 and 5.3 provide examples of how non-algorithmic 

analyses of water quality data can be useful. The tables 

also identify some pitfalls to be avoided when interpreting online data. Additional data on the response of water quality sensors to biological suspensions and culture 

broth are presented in Tables 5.4 through 5.7. 

5.1.2 Recirculating DSS Loop No. 6 Data 

Analysis

The data tabulated and reported in this section were 

generated by injecting selected contaminants (as 

listed in the individual tables) into recirculating DSS 

Loop No. 6 (described in Section 2.1.1) with chloraminated water prepared at the T&E Facility. The 

preparation methodology for the chloraminated water 

was described previously in Section 2.2.1. For performing these tests, the background chloramine level 

Table 5.3 Normalized, Signal-to-Noise Corrected Sensor Parameter Response to Injected Chemical Contaminants in

Chlorinated Water – Single Pass DSS

Chemical 

Contaminants

Initial In-Pipe 

Concentration

(mg/L)Free ChlorineTotal ChlorineChloride (Cl-

)

Specific ConductanceDissolved Oxygen (DO)Oxidation Reduction Potential (ORP)pHTurbidity

Aldicarb 0.2 -31.6 -20.2 3.5 1.2 -0.5 -2.1 2.1 2.5

1.1 -101.0 -155.1 -5.3 0.0 -3.6 -9.8 0.5 4.8

2.2 -309.2 -216.6 6.3 0.0 0.3 -40.6 -1.9 -1.1

Glyphosate 0.4 -90.2 -40.0 4.6 -0.0 4.1 0.6 -1.3 6.0

1.5 -275.7 -97.6 11.7 -5.4 3.8 -14.4 -28.5 2.2

3.0 -380.3 -130.4 14.9 -0.3 2.8 -34.1 -108.4 -0.8

Colchicine 0.4 -4.7 0.5 0.1 1.5 0.8 2.7 4.4 2.6

1.8 -7.2 -9.8 0.1 3.2 -0.2 -4.4 3.6 4.5

3.6 -19.6 -9.4 -3.8 0.0 -3.0 -13.1 2.7 2.9

Dicamba 0.8 -5.9 -0.0 -5.7 -0.4 -7.4 7.2 -4.9 0.0

1.3 -6.3 -5.4 -12.7 -3.1 -8.0 9.9 -9.4 -2.7

2.6 -3.3 -0.5 -3.2 1.6 -6.6 11.4 -13.1 -2.5

Dimethyl Sulfoxide 0.6 -23.4 -22.1 -4.5 0.0 -6.2 4.1 4.8 0.7

2.0 -52.5 -110.8 0.3 2.6 -8.1 -4.6 5.2 1.9

4.0 -72.7 -115.3 1.9 0.4 -8.2 0.0 1.9 1.8

Lead Nitrate 0.6 -3.7 2.1 -7.5 0.3 2.2 2.4 -3.1 8.9

0.7 -2.4 -11.3 -2.8 -1.9 -3.1 -0.4 -1.0 3.9

1.4 -2.1 -4.0 -9.5 -0.5 0.5 -7.8 4.9 16.3

Mercuric Chloride 0.4 -6.1 -4.8 -4.7 -5.4 -0.3 3.4 -1.7 1.4

1.1 -2.2 -4.3 -13.1 5.5 -1.7 8.9 -26.8 4.9

2.2 -2.7 0.6 -10.0 1.7 -2.3 26.6 -33.0 0.7

Nicotine 0.4 -26.8 -10.0 2.0 1.0 3.6 -2.4 5.4 5.7

1.9 -187.2 -43.0 -2.1 0.2 2.8 -20.1 8.5 0.6

3.8 -243.6 -91.7 23.1 0.6 -2.4 -59.2 13.5 1.0

Potassium 

Ferricyanide

0.6 -7.8 -0.4 12.6 0.9 -2.9 3.4 2.3 2.1

1.6 -7.5 13.1 58.3 4.9 -2.2 -3.0 -3.1 1.4

3.2 -5.3 37.1 49.7 4.8 -0.9 -3.6 -5.7 0.4

Sodium Thiosulfate 

(Anhydrous)

0.2 -38.8 -37.0 3.9 4.2 -1.2 -1.9 2.6 3.6

1.3 -235.1 -233.2 12.2 7.0 -0.3 -14.9 -6.5 3.4

2.6 -158.4 -193.8 29.8 5.9 -2.5 -29.5 -4.3 3.3

Sucrose 0.6 -8.9 -1.6 -0.5 1.8 -0.1 3.5 0.6 0.1

1.8 -6.0 -2.7 -4.6 0.4 -10.4 6.0 -1.8 1.0

3.6 -4.8 1.0 -5.0 0.2 -3.4 4.0 -2.5 3.0

Control Blank 0 2.3 4.9 3.2 3.2 2.0 4.0 2.7 5.7

0 2.0 0.6 0.5 2.5 1.5 2.2 0.7 1.9

AVGa 2.2 2.7 1.9 2.9 1.8 3.1 1.7 3.8

 indicates that the normalized, S/N corrected change was >10 of the baseline within 15 minutes. a Average of two control blank runs. Data presented in this table was not corrected to accommodate for the control blank response.
5-4

was set at 2 mg/L (measured as total chlorine). The 

background values of other measured water quality 

parameters are the same as mentioned previously in 

Section 5.1.1. Table 5.8 shows the quantitative sensor responses to contaminants injected in DSS Loop 

No. 6 as absolute change, percent change, and S/N 

ratio. Also, Table 5.8 shows that the TOC parameter 

responded to each contaminant except sodium arsenite, which was expected since sodium arsenite is not 

an organic (carbon-containing) compound. The TOC 

sensor responses are comparable to those observed 

during the same injections in the chlorinated GCWW 

Biological 

Contaminants and 

Growth Media

Initial In-Pipe 

Concentration

(mg/L)Free ChlorineTotal ChlorineChloride (Cl-

)

Specific ConductanceDissolved Oxygen (DO)Oxidation Reduction Potential (ORP)pHTurbidity

Nutrient Broth 0.12 -1.4% -2.2% 0.1% 0.9% -2.3% -1.1% -0.2% -20.7%

0.48 -9.1% -4.1% -0.1% 0.2% -0.5% -1.4% -0.1% -14.1%

0.96 -21.6% -6.4% 1.1% 0.2% -1.6% -1.1% -0.1% 3.3%

Trypticase Soy Broth 0.12 -6.7% -4.6% -1.1% 0.2% -0.2% -0.1% -0.1% 60.0%

0.48 -13.4% -9.3% -0.3% -0.0% 0.1% -2.0% -0.2% 0.4%

0.96 -20.4% -13.0% 0.2% 0.1% -0.4% -4.4% -0.0% 0.0%

Terrific Broth 0.12 -3.0% 1.0% 2.7% 0.0% 0.8% 0.5% 0.2% 15.3%

0.47 -13.8% -3.6% -1.0% -0.1% -0.1% -0.4% 0.2% -19.3%

0.97 -25.5% -6.4% -0.7% 0.1% -0.2% -1.2% 0.5% -3.0%

E.coli in Terrific Broth 0.01 -20.1% -7.0% 1.8% 0.1% -0.9% -1.9% 0.1% 170.8%

0.07 -70.5% -12.4% 4.6% 0.3% -0.7% -5.8% -0.6% 192.5%

0.14 -89.2% -9.6% 10.7% 0.8% -0.9% -7.6% -1.3% 296.9%

Control Blank 0 2.4% 1.6% 5.5% 0.5% 0.5% 1.8% 0.2% 52.5%

0 1.0% 0.2% 0.6% 1.1% 1.0% 2.0% 0.1% 71.8%

AVGa 1.7% 0.9% 3.1% 0.8% 0.8% 1.9% 0.2% 62.1%

 indicates that the percent change was >10% of the baseline within 15 minutes. a Average of two control blank runs. All data presented in this table was corrected to accommodate for the expected average control blank response.

Table 5.4 Percent Change in Sensor Parameter Response to Injected Biological Contaminants and Growth Media in 

Chlorinated Water – Single Pass DSS

Biological 

Contaminants and 

Growth Media

Initial In-Pipe 

Concentration

(mg/L)Free ChlorineTotal ChlorineChloride (Cl-

)

Specific ConductanceDissolved Oxygen (DO)Oxidation Reduction Potential (ORP)pHTurbidity

Nutrient Broth 0.12 -9.4 -4.4 0.5 4.9 -0.6 -4.2 -3.5 -2.4

0.48 -13.3 -13.8 -0.4 0.6 -1.1 -11.6 -2.9 -1.3

0.96 -38.2 -18.6 6.1 3.0 -3.1 -18.8 -3.2 1.3

Trypticase Soy Broth 0.12 -8.7 -12.8 -3.5 0.7 -0.1 -1.4 -1.9 1.8

0.48 -20.2 -31.8 -1.7 -3.7 1.2 -26.7 -5.5 -0.4

0.96 -24.0 -25.8 0.2 0.4 -1.1 -47.7 1.8 0.0

Terrific Broth 0.12 -6.4 0.8 2.6 -1.4 2.6 3.4 1.8 1.0

0.47 -43.2 -4.8 -3.8 -1.0 1.1 -7.4 4.0 0.5

0.97 -65.6 -9.7 -6.2 1.4 1.1 -16.4 6.7 0.7

E.coli in Terrific Broth 0.01 -21.2 -15.9 2.2 1.0 -2.8 -34.4 0.5 2.1

0.07 -127.5 -20.2 5.8 2.5 -2.0 -51.0 -10.0 2.2

0.14 -153.6 -29.0 5.6 5.7 -3.2 -59.6 -22.6 2.8

Control Blank 0 2.3 4.9 3.2 3.2 2.0 4.0 2.7 5.7

0 2.0 0.6 0.5 2.5 1.5 2.2 0.7 1.9

AVGa 2.2 2.7 1.9 2.9 1.8 3.1 1.7 3.8

 indicates that the normalized, S/N corrected change was >10 of the baseline within 15 minutes. a Average of two control blank runs. All data presented in this table was corrected to accommodate for the expected average control blank response.

Table 5.5 Normalized, Signal-to-Noise Corrected Sensor Parameter Response to Injected Biological Contaminants 

and Growth Media in Chlorinated Water – Single Pass DSS

B. globigii

Initial In-Pipe 

ConcentrationFree ChlorineTotal ChlorineSpecific ConductanceOxidation Reduction Potential (ORP)Turbidity

Unwashed 1.9E+05b -19.0% -12.0% 0.2% -1.3% -100.0%

Washed 2.4E+04b -3.7% -2.5% -0.1% -0.3% -100.0%

Control Blank AVGa 1.7% 0.9% 0.8% 1.9% 62.1%

 indicates that the percent change was >10% of the baseline within 15 minutes. a Average of two control blank runs. Data presented in this table was not corrected to 

accommodate for the control blank response. b Concentration in Cells/mL.

Table 5.6 Percent Change in Sensor Parameter 

Response to B. globigii Injection in

Chlorinated Water – Single Pass DSS

B. globigii

Initial In-Pipe 

ConcentrationFree ChlorineTotal ChlorineSpecific ConductanceOxidation Reduction Potential (ORP)Turbidity

Unwashed 1.9E+05b -27.3 -37.7 4.5 -11.3 -2.4

Washed 2.4E+04b -10.7 -6.8 0.8 -3.1 -0.1

Control Blank AVGa 2.2 2.7 2.9 3.1 3.8

 indicates that the normalized, S/N corrected change was >10 of the baseline within 15 

minutes. a Average of two control blank runs. Data presented in this table was not corrected to 

accommodate for the control blank response. b Concentration in Cells/mL.

Table 5.7 Normalized, Signal-to-Noise Corrected Sensor 

Parameter Response to B. globigii Injection in

Chlorinated Water – Single Pass DSS
5-5

Table 5.8 Quantitative Sensor Parameter Response Matrix to Contaminants in Chloraminated Cincinnati Tap Water

Chemical 

Contaminants

Initial In-Pipe ConcentrationTotal Organic CarbonTotalChlorine ChlorideAmmonia NitrogenNitrate NitrogenSpecific ConductanceOxygen Reduction PotentialpHTurbidity

(mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (μS/cm) (mV) (NTU)

Glyphosate 

(Roundup®) 1

0.27a

127.5%

15.7

-0.04

1.6%

2.8

N/Ab

0.1

1.0%

2.7

6.33

3.8%

6.1

5.16

0.9%

10.0

N/A

-0.02

0.2%

3.1

0.56

111.1%

46.2

Malathion (Real Kill®) 1

0.40

38.2%

54.7

-0.04

1.8%

2.1

5.2

7.7%

3.8

0.08

1.5%

1.5

12.85

6.1%

12.9

2.11

0.3%

4.8

-5.6

1.7%

8.0

0.03

0.3%

4.8

0.46

112%

43.1

Phorate 1

0.22

18%

48.4

-0.22

9.5%

60.0

N/A

-0.04

1.5%

0.4

8.73

17%

34.1

2.57

0.6%

7.1

-7.7

2.2%

16.2

-0.02

0.2%

3.4

0.29

74.9%

11.5

Wastewater 0.8v/vc

(2 gallons)

0.18

13.8%

34.0

-0.04

1.6%

5.7

N/A

0.06

2.1%

0.5

29.73

11.1%

27.8

18.13

3.7%

28.4

-7.0

4.8%

6.2

-0.05

0.5%

7.2

0.43

116%

23.1

Sodium Arsenite 1

0.02

1.7%

3.8

-0.31

14.6%

48.6

20.7

9.5%

4.7

0.29

4.8%

2.7

4.22

1.7%

1.7

0.94

0.2%

1.7

-74.2

23.9%

84.2

0.05

0.6%

6.9

0.53

139%

9.3

Nicotine 10

2.71

963%

269.8

-0.02

0.8%

5.6

2.3

1.7%

6.6

N/A

1.13

9.2%

4.5

3.05

0.6%

5.3

-10.3

2.8%

18.0

0.02

0.3%

5.1

1.55

505%

173.3

Control Blank 0

0.02

1.7%

2.1

-0.01

0.4%

2.0

0.00

 0.0%

0.0

0.03

1.1%

2.8

0.01

0.1%

0.2

4.35

0.9%

8.4

-1.0

0.3%

0.0

-0.02

0.3%

5.3

0.73

160%

19.5

a Top values in each cell are the magnitude of the change, middle values are percent change, and bottom values are signal-to-noise ratio.

b N/A indicates a problem with the probe or SCADA system that rendered the data invalid.

c Two gallons of wastewater were injected, which represents 0.8 percent of the loop volume.

water. Responses were also similar for other sensors, 

except total chlorine. Total chlorine, as a measure of 

chloramines, showed little change for the contaminants tested, except for decreases of 0.22 mg/L (9.5 

percent decrease, S/N: 60) and 0.31 mg/L (14.6 percent decrease, S/N: 48.6) for phorate and sodium arsenite, respectively. 

However, since chloramines (mostly monochloramine) 

react more slowly with the injected organic contaminants than with free chlorine, the changes reported 

in Table 5.8 occurred over a period of four hours. In 

comparison, the free chlorine values changed almost 

instantaneously when the same contaminants were introduced into DSS Loop No. 6. So it is important to 

note that the changes in Table 5.8 are not necessarily 

comparable to those presented in the previous section. 

Furthermore, the slow changes in chloramine levels 

(measured as total chlorine) might prevent the person 

(or automated computer algorithm analyzing this data) 

from detecting a significant change unless the time 

window for this type of analysis was appropriately adjusted to account for the delay (Szabo et al., 2006 and 

2008a; Kroll and King 2007).

5.1.3 Edgewood Chemical and Biological 

Center Test Loop Data Analysis

The data tabulated and reported in this section was generated by injecting selected contaminants (as listed in 

the individual tables) into the recirculating ECBC Test 

Loop with chlorinated water supplied by the City of Aberdeen Department of Public Works (Aberdeen, Maryland). The sensor responses to introduction of chemical warfare agents in the recirculating ECBC Test Loop 

are presented in Tables 5.9 (% change) and 5.10 (S/N). 

The ECBC Test Loop is similar to DSS Loop No. 6 described in Section 2.1.2. Both Tables 5.9 and 5.10 indicate that TOC and free/total chlorine are good detection 

parameters for all injected contaminants. Also, the small 

changes to pH result in a low percent change, but S/N 

data analysis presented in Table 5.10 shows that pH is 

a very good detection parameter for the warfare agent 

contaminants, given that the baseline is very stable. 

The data presented in Sections 5.1.1 through 5.1.3 

clearly indicates the usefulness of analyzing data in a 

qualitative and/or quantitative fashion, but the analyst 

should be aware of the usefulness and pitfalls of each 

selected approach. In addition, the professional judg-
5-6

ment of the analyst was used to define the usable significant change thresholds such as absolute change, 

percent change, and S/N ratio. For example, the peak 

sensor value used in the absolute change and subsequent calculations was computed based on values obtained between the time that the contaminant contacted 

the sensor and 15 minutes past that time. This definition 

of “time window” was not necessary for parameters 

like pH or conductivity, which respond within a minute. 

However, responses for parameters such as ORP develop more slowly and the maximum change might not be 

reached for an extended period. Hence, the definition 

of time window is necessary to utilize the full sensor 

response. Even though free/total chlorine parameters 

are good indicators, they might react slowly depending 

upon the injected contaminant and thus delay the observation of significant change in the disinfectant residual. 

This delay is much longer for chloraminated systems. 

Furthermore, the threshold at which a statistical parameter such as percent change or S/N ratio becomes 

significant or triggers a change alarm is based on the 

judgment of the analyst and the baseline water quality 

at the monitoring point. In general, absolute changes of 

10% and S/N ratio of 10 were deemed adequate to idenInjected Agent

Initial In-Pipe 

Concentration

(mg/L)Free ChlorineTotal ChlorineTotal Organic Carbon (TOC)Specific ConductanceOxidation Reduction Potential (ORP)pHTurbidity

V-series Nerve Agent 0.02 -5.7% -9.9% 43.8% 1.1% N/A 1.1% -14.3%

0.2 -20.2% -19.0% 20.4% 0.4% N/A -1.3% -7.8%

2.0 -58.8% -48.3% 135.4% 0.9% N/A 2.5% -4.0%

G-type Nerve Agent 0.02 -4.7% -4.6% 15.3% 0.3% N/A 3.6% -6.5%

0.2 -14.3% -7.5% 20.4% 1.6% N/A 3.8% -12.7%

2.0 -13.2% -7.3% 82.9% 1.0% N/A 5.7% -9.7%

Ricin 0.25 -40.2% -18.3% 12.9% 1.6% -0.2% -0.7% 1.0%

0.25 -21.4% -13.8% 14.3% 1.0% N/A 1.2% -0.2%

0.25 -29.1% -14.5% 20.8% 2.2% N/A 3.1% -8.4%

Potassium Cyanide 0.2 -90.6% -68.4% 21.3% 1.4% -8.9% 2.5% 1.7%

0.2 -77.9% -63.7% 10.0% 1.0% -11.4% 4.0% 1.9%

2.0 -98.7% -98.5% 53.4% 5.6% -59.8% 17.1% 7.5%

 indicates that the percent change was >10% of the baseline within 15 minutes.

Table 5.9 Percent Change in Sensor Parameter Response to Injected Warfare Agents in

Chlorinated Water – Edgewood Chemical and Biological Center Test Loop

Injected Agent

Initial In-Pipe 

Concentration

(mg/L)Free ChlorineTotal ChlorineTotal Organic Carbon (TOC)Specific ConductanceOxidation Reduction Potential (ORP)pHTurbidity

V-series Nerve Agent 0.02 1.4 13.4 647.4 7.1 N/A 5.9 38.3

0.2 16.7 45.7 19.5 1.9 N/A 33.8 21.6

2.0 47.7 45.5 41.0 3.8 N/A 7.1 8.0

G-type Nerve Agent 0.02 3.3 9.1 19.7 2.5 N/A 12.2 11.0

0.2 9.8 16.7 20.3 6.6 N/A 16.2 25.3

2.0 7.2 7.0 152.4 6.0 N/A 21.0 30.7

Ricin 0.25 17.6 17.8 21.3 8.6 1.5 10.2 1.4

0.25 6.0 15.7 28.3 5.3 N/A 5.9 0.2

0.25 21.4 16.3 41.6 8.3 N/A 14.5 12.0

Potassium Cyanide 0.2 35.5 69.7 17.0 10.1 44.8 9.7 1.8

0.2 51.6 88.6 17.8 6.7 34.6 39.9 2.7

2.0 56.9 119.3 184.8 25.4 89.3 80.5 6.4

 indicates that the normalized, S/N corrected change was >10 of the baseline within 15 minutes.

Table 5.10 Normalized, Signal-to-Noise Corrected Sensor Parameter Response to Injected Warfare Agents in

Chlorinated Water – Edgewood Chemical and Biological Center Test Loop

tify the parameters best suited for detecting contamination events at the T&E Facility, but these numbers will 

vary depending on the variability of the baseline water 

quality data for a particular location. Ultimately, data 

analysis employing these techniques need to be automated and performed real-time for realizing any event 

detection benefits.

5.2 Automated Algorithmic 

Evaluation of Sensor Response

Based on the online data collected during the tests performed at the EPA T&E Facility and at the WSi field 

locations, EPA confirmed the need for automated data 

analysis tools. Specifically, this includes data analysis 

tools that can distinguish between normal variations in 

background water quality and changes in water quality 

triggered by the presence of contaminants (O’Halloran 

et al., 2009). Often referred to as event detection algorithms, such data analysis tools can read SCADA data 

(water quality signals, operations data, etc.), perform 

analysis in near real-time, and return an event value 

or code 0/1 (indicating the presence or absence of an 

alarm). 
5-7

EPA	collaborated	with	SNL	to	develop	the	open	source	

CANARY	software,	which	is	intended	to	provide	this	

capability.	 CANARY	 software	 is	 designed	 to	 accept	

standard	 water	 quality	 data	 and	 use	 mathematical	 algorithms	to	identify	the	onset	of	periods	of	anomalous	

water	quality,	while	at	the	same	time	limiting	the	number	of	false	alarms	that	occur.	CANARY	is	trained	on	

“normal”	baseline	water	quality	data	by	the	user	during	

the	setup,	and	the	configuration	parameters	are	selected	

to	accommodate	the	normal	site-specific	variability	of	

water	 quality	 parameters.	 Therefore,	 these	 configuration	parameters	could	vary	from	one	utility	to	the	next	

and	might	even	vary	across	monitoring	locations	within	

a	single	utility.	CANARY	can	be	set	up	to	receive	data	

from	a	SCADA	database,	and	return	alarms	to	the	SCADA	system.	In	addition,	it	can	be	run	“offline”	on	historical	data	to	help	set	the	configuration	parameters	(or	

train	the	algorithm)	to	provide	the	desired	balance	between	event	detection	sensitivity	and	false	alarm	rates.

CANARY’s	open	source	code	is	designed	to	be	customizable,	allowing	outside	researchers	to	develop	new	algorithms	that	can	be	added	to	CANARY.	In	the	current	

version	(Version	4.2),	CANARY	has	three	change	detection	 algorithms:	time	 series	increments,	 a	linear	 filter,	

and	 a	 multivariate	 nearest-neighbor	 algorithm.	 These	

algorithms	identify	a	background	“water	quality	signature”	 for	 each	 water	 quality	 sensor	 and	 compare	 each	

new	 water	 quality	 measurement	 to	 the	 background	 to	

determine	if	the	new	measurement	is	an	outlier	(anomalous)	 or	 not.	The	 definition	 of	the	water	 quality	 background	 is	 updated	 continuously	 as	 new	 data	 become	

available.	A	binomial	event	discriminator	(BED)	examines	multiple	outliers	within	a	prescribed	time	window	

to	determine	the	onset	of	either	an	anomalous	event	or	

a	change	in	the	water	quality	baseline.	Figure	5.1	shows	

the	schematic	operation	of	CANARY	software.

In	 addition	 to	 the	 CANARY	 software,	 EPA	 has	 also	

tested	the	 commercially	 available	Hach	Event	MonitorTM	 Trigger	 System.	 Hach’s	 patented	 technology	

utilizes	the	Hach	Event	MonitorTM	Trigger	System	to	

analyze	 five	 commonly	 measured	 water	 quality	 parameters	monitored	from	the	Hach	WDMP	(chlorine,	

pH,	turbidity,	conductivity)	and	the	Hach	astroTOCTM	

UV	 process	TOC	analyzer	to	estimate	a	water	 distribution	system’s	operating	baseline	(i.e.,	water	quality	

under	normal	operating	conditions).	Thereafter,	every	

minute,	 the	 Hach	 Event	 MonitorTM	 Trigger	 System	

analyzes	the	 sensor	 data	 and	 computes	 a	trigger	 signal,	 which	 indicates	 the	 level	 of	 deviation	 from	 the	

water	 quality	 baseline.	 If	 significant	 deviations	 occur,	 the	 trigger	 signal	 sends	 alarms	 to	 the	 operators	

in	 real-time.	 Once	 a	 deviation	 is	 detected,	 the	 Hach	

Event	MonitorTM	Trigger	System	signals	the	(optional)	

automatic	water	sampler	to	capture	a	water	sample	at	 Figure 5.1 CANARY Operation Schematic

Water Utilities and

Sensor Manufacturers

•	 Online	monitoring	data	should	be	evaluated	both	

qualitatively	and	quantitatively	to	identify	which	

parameters	provide	significant	change	signals	and	

have	relatively	stable	baselines	with	low	S/N	ratio.	

These	criteria	should	be	factored	into	the	selected	

algorithmic	approach	of	data	analysis.

•	 Free	chlorine	and	TOC	were	found	to	be	the	

most	responsive	trigger	parameters	in	chlorinated	

systems.	Total	chlorine	was	not	an	effective	trigger	

parameter	in	chloraminated	systems.	TOC	or	TOC	

surrogate	monitoring	should	be	considered	for	both	

chlorinated	and	chloraminated	systems.

•	 To	capture	and	evaluate	sensor	responses	in	

real-time,	SCADA	equipment	and	algorithmic	

analysis	are	highly	recommended.	The	SCADA	

database	should	be	designed	so	that	it	can	be	easily	

interfaced	with	one	or	more	automated	algorithms	

for	real-time	analysis	of	data.

•	 Algorithms	should	be	designed	to	learn	or	predict	

baseline	values	of	parameters	when	monitoring	

location(s)	with	relatively	unstable	baseline	

conditions.	Known	routine	system	events	(such	as	

valve	closures	or	tank	fill	and	discharge	cycles)	

need	to	be	incorporated	into	the	algorithmic	

evaluation(s)	to	reduce	false	positive	events.

•	 For	chlorinated	systems,	the	algorithms	and	

sensors	should	be	designed	to	co-relate	free	

chlorine	and	TOC	data.	Manufacturers	should	

consider	providing	alarming	or	algorithm	software	

with	the	online	sensor	equipment	that	is	capable	

of	identifying	bad	data	and	other	instrument	

operational	problems.	This	will	prevent	bad	data	

from	being	analyzed	by	the	algorithms,	resulting	in	

fewer	false	positive	events.

Read Configuration and Setup

Read

New Water Quality Data

Process

Event Detection Algorithms

Report

Probable Events

Print Results Files and Exit

Wait For

Next Time Step

When Finished
5-8

the designated monitoring location. The system subsequently compares the computed algorithmic values 

to the “Agent Library” and “Plant Library” to classify 

the deviation. The subscription-based “Agent Library” 

(Hach GuardianBlueTM Early Warning System) contains “fingerprints” for a wide variety of threat contaminants, ranging from V-series nerve agent (VX) and 

ricin to arsenic and herbicides. The site-specific operator-developed “Plant Library” contains “fingerprints” 

of operational and naturally occurring events specific 

to each water distribution system. The plant library can 

be used to detect and classify real-world events such as 

water main breaks, switching water sources, and caustic overfeeds.

EPA is also evaluating other commercial event detection technologies such as the Frontier Technology, Inc. 

(FTI) Event Detection Software (EDS) tool called H2O 

SentinelTM for contaminant detection. FTI has developed a proprietary software to monitor a set of standard 

water quality parameters measured by sensor stations 

placed within a utility’s water distribution system and 

detect anomalous events that might be indicative of 

possible contamination incidents.

EPA plans to continue evaluating other commercially 

available event detection algorithms as they become 

available (Einfeld et al., 2007; Umberg et al., 2009). 

Because true contamination events are rare, the performance of event detection systems is difficult to evaluate. It is tempting to set the sensitivity of the algorithm 

at a low level so that few alarms are generated, since 

true contamination events are costly to investigate. 

However, high sensitivity algorithms can result in the 

generation of many alarms, which can result in “alarm 

fatigue.” EPA continues to evaluate the alarm prediction accuracy of these algorithms by simulating contamination events. EPA is considering the use of modified receiver operating characteristic (ROC) curves, a 

data classification methodology that can plot the fraction of true positive alarms versus the fraction of false 

positive alarms generated by the individual algorithm. 

Modified ROC curves can help determine the efficiency 

of these algorithmic approaches.

5.3 Data Analysis Best Practices 

When the contaminant (or surrogate) injection tests 

were performed at the T&E Facility, the algorithmic 

approaches of data evaluation were not available, with 

the exception of the previously-mentioned ETV study. 

The qualitative approach, although robust, is not a viable technique for real-time event detection. There 

are normal/natural changes in water quality that could 

mimic some of the qualitative changes shown in the 

testing. For example, at a monitoring station near a storage tank or reservoir, the chlorine levels might change 

dramatically depending upon the source of water and 

tank operation. Similarly, quantitative changes have a 

drawback: in real life, the concentration of the injected 

contaminants is unknown, and the resulting amount of 

change does not necessarily correlate with prescribed 

quantitative values. Therefore, an algorithmic approach 

is the preferred approach for event detection. However, 

a utility with existing SCADA systems (which generally allow for high-low alarm set points) in the process of 

deploying water quality monitoring stations can utilize 

significant threshold and other non-algorithmic methodologies described in this chapter to alert the local operator for further investigation. These types of preliminary data evaluations will assist in establishing and fine 

tuning parameter-specific change thresholds and timewindows for the algorithmic approaches. The evaluations of existing algorithms have so far demonstrated 

only limited success. Also, in an algorithmic approach, 

there is a need to optimize the sensitivity of the algorithm so that the false positives are minimized while 

retaining the algorithm’s ability to detect contamination 

events. EPA’s research for fine tuning individual algorithms and the search for new approaches are ongoing.
6-1

6.0 Operation,

Maintenance and 

Calibration of Online 

Instrumentation

The capital costs of the equipment tested at the EPA 

T&E Facility are readily available from the manufacturers and are subject to change. However, the O&M 

costs are not as readily obtainable or well-defined. 

The experience gained at the T&E Facility indicates 

that, for most online sensors, O&M costs will quickly 

exceed capital costs. To keep the costs under control 

for the longer term, manufacturer recommendations 

should be followed when performing O&M activities. 

Also, maintenance requirements vary significantly, 

depending upon the parameter and the device. The online instrumentation evaluated at the T&E Facility was 

calibrated and maintained as needed for testing efforts 

described in the previous chapters. For year-round operation, maintaining a tight maintenance schedule is 

necessary to obtain optimal instrument performance. 

The scheduled maintenance activity, as well as cost of 

consumable(s), depends on the individual sensor. 

This chapter describes general O&M activities and associated costs for the instruments evaluated at the T&E 

Facility. For this purpose, EPA tracked the labor and 

consumable costs required to operate and maintain the 

tested sensor equipment. The O&M costs presented 

here do not include travel time to service the individual 

monitoring locations. The travel costs will vary significantly, depending upon where service personnel are 

operating relative to the geographic distribution of the 

monitoring sites. Furthermore, the total labor cost will 

depend on operator skill and training, sensor complexity, service contract terms, and the number of sensor 

stations deployed. Based on discussions with utilities 

participating in the WSi, the optimal service requirement for a sensor station deployed to the field was determined to be one O&M site visit per month, with each 

visit taking less than four hours. 

6.1 Operation & Maintenance 

Labor Costs

The labor hours expended for O&M vary significantly, 

depending upon the type of instrument used (or parameter that is monitored). As mentioned previously, the goal 

of the WSi’s pilot implementation in Cincinnati was to 

achieve a service level of four labor hours per monitoring station per month. It was well understood that, during the initial phase of installation and shake-down, the 

labor costs were going to be much higher. Also, depending upon the size and complexity of an implementation, 

the shake-down period can range from a month to a 

year. The labor hours per instrument also vary significantly. Of the conventional instruments (TOC, chlorine, 

conductivity, pH/ORP and Turbidity), TOC instruments 

were by far the most labor intensive from an O&M perspective. They also required the highest technician skill 

level to operate and maintain. However, the TOC instruments are being continuously redesigned and the labor 

level required is expected to be lower in the coming 

years. Overall, the data collected from the WSi initiative 

(for a 10-month period between January and September 

2008) showed that approximately 1.5 person(s) working 

on a full-time basis were needed to operate and maintain 

17 monitoring stations. Between 60% and 80% of this 

labor cost was associated with the O&M for the TOC instruments. Also, as indicated previously, the labor hour 

estimate does not include travel time to the monitoring 

stations, which could vary significantly depending upon 

the geographic configuration of the monitoring network. 

Experience indicates that labor estimates per site can 

vary widely because there might be some sites with adverse water quality (or other site-specific anomaly) that 

can cause numerous O&M problems. The best current 

estimate for labor hours per site is 1.5 days per month on 

average, which is three times the goal of the WSi. 

Additional labor costs will be incurred for data analysis and event detection efforts. Because it is too labor intensive to have the operator monitor the data on 

a continuous basis, an automated event detection or 

alarm system is necessary. It is expected that the existing SCADA operator at a water utility can be assigned 

the additional duty of checking the operational status of 

the data collection system during every shift to ensure 

that data is being collected and analyzed by automated 

tools. To ensure everything is operating normally, the 

utility should plan to assign a person to skim through 

the historical data and monitor the alarm software on a 

daily basis. This task is expected to add approximately 

30 to 60 minutes per day or per shift depending upon 

how the task is assigned. The reviewer should be a staff 

member who already understands the operations and 

the monitored water quality data and can make decisions on O&M and alarm response needs as warranted 

by the quick review of the data and alarm history.

6.2 Equipment-Specific 

Maintenance and Consumable 

Costs

As mentioned previously, the maintenance requirements 

vary significantly, depending upon the parameter and 

the device. Based on the experience gained at the T&E 

Facility, EPA identified TOC, free chlorine, conductivity, pH/ORP, turbidity and temperature as the key online 
6-2

monitoring parameters. These parameters are listed in 

the order of importance from an event detection perspective. The following sections (Section 6.3 through 

6.7) describe these parameters (except temperature) and 

the instrument-specific O&M activity for the equipment 

evaluated at the T&E Facility. The temperature probes 

are inexpensive and very robust with almost no maintenance requirements and are, therefore, not discussed 

in this report. The prices for consumables for the instruments mentioned in this chapter are based on pricing 

information obtained during the years 2007 and 2008.

6.3 Total Organic Carbon 

Instrumentation

TOC instrumentation responded to a wide range of 

contaminants tested at the T&E Facility. Especially in 

chloraminated waters, TOC instrumentation was more 

responsive to contaminants than the instrumentation 

used for measuring total chlorine/monochloramines. A 

skilled technician was required to reliably operate and 

maintain the TOC instrumentation on a continuous basis, making it the most expensive and time-consuming 

equipment to operate. It is highly recommended that the 

person responsible for TOC instrumentation O&M issues obtain manufacturer-provided training, or the water utility should consider purchasing a manufacturer 

maintenance contract. EPA also evaluated an optical instrument (Carbo::lyserTM) that estimates TOC levels by 

measuring UV-Vis spectra between 200 and 750 nanometer (nm) wavelengths. 

6.3.1 Hach astroTOCTM UV Process Total 

Organic Carbon Analyzer

This instrument requires monthly replenishment of reagents, quarterly scheduled maintenance, and approximately $4,000 per year for consumables. The following 

routine maintenance activities need to be performed:

• Replace reagent/acid (monthly), reagent/oxidizer 

(quarterly)

• Change the pump tubing (quarterly)

• Replace the semi-permeable sparger membrane 

(quarterly)

• Replace the hydrophobic filter (quarterly)

• Calibrate the infra-red detector with carbon 

dioxide (quarterly)

• Calibrate the wet-side of the unit with potassium 

hydrogen phthalate (KHP) standard (monthly/

quarterly)

• Replace the UV lamps (annually)

• Replace the consumables in the carrier gas 

generator (annually)

• Clean infra-red detector window (annually)

A significant amount of time was required for troubleshooting and repairing the Hach astroTOCTM UV Process Total Organic Carbon Analyzer, primarily due to 

instrument malfunctions. The types of malfunctions 

observed were mostly related to plugged or interrupted 

flow (liquid/gaseous):

• There was a build-up of silica crystals in the 

resample block, which was rectified by Hach by 

redesigning the resample block

• The sparger orifice would get plugged (semipermeable membrane) and result in shut-down 

of the instrument

Monthly calibration is essential to electronically adjust 

for the instrument drift prior to the quarterly maintenance/calibration event.

6.3.2 Sievers® 900 On-Line Total Organic 

Carbon Analyzer

This instrument requires quarterly scheduled maintenance. Depending upon the initial water quality, maintenance costs can vary between $2,000 and $4,000 per 

year for consumables. The following routine maintenance activities need to be performed:

• Replace reagent/acid/oxidizer (quarterly/semiannual)

• Change the pump tubing (semi-annual)

• Replace the resin bed (semi-annual)

• Replace UV lamp (semi-annual)

• Replace ICR degasser, chemical trap, and pump 

rebuilt kit (annual)

• Replace in-line particulate filter (annual)

• Replace oxidizer syringe (annual)

• Replace restrictor tubing (annual)

Comparatively, a significant amount of time was required for troubleshooting and repairing the Sievers® 900 On-Line TOC Analyzer, primarily due to 

instrument malfunctions. Malfunctions observed 

were mostly related to plugged or interrupted liquid 

flow (e.g., restrictor tube blockage). In areas with 

high-carbonate water, reagents need to be replenished 

quarterly.

6.3.3 Spectro::lyzerTM/Carbo::lyzerTM

As indicated earlier, this sensor is an optical instrument that estimates TOC levels by measuring UV-Vis 

spectra between 200 and 750 nm wavelengths. Only 
6-3

TOC that has some absorption property in the specified 

wavelengths is detected. The instrument has minimal 

O&M requirements. At first, EPA procured a unit made 

of high-grade aluminum. However, the body of this instrument corroded in chlorinated Cincinnati tap water. 

A replacement stainless steel unit was provided by the 

manufacturer free of charge. EPA recommends that 

only a stainless steel unit be purchased for locations 

with water quality that might be aggressive to aluminum. The unit in global calibration mode does not require any calibration. If local TOC values are known, 

the unit can be calibrated using locally available high 

and low values. For obtaining a zero value in the local 

calibration mode, a highly-purified High-Performance 

(or High-Pressure) Liquid Chromatography (HPLC) 

grade reagent or a high grade distilled water should be 

used. Deionized and nanopure waters are not suitable 

for this purpose.

6.4 Chlorine Instrumentation

Free chlorine instruments responded to the majority of 

contaminants tested at the T&E Facility on chlorinated 

Cincinnati tap water. Both free and total chlorine instruments use either a reagent-based (colorimetric) method 

or a reagent-free method (amperometric/polarographic/

galvanic – electrode/membrane-based). The reagentfree method has varied pH dependence, depending upon 

the use of buffering agents. The reagent-based method 

consumables include buffering and indicator solutions. 

The reagent-free method consumables include electrolyte solutions (and membranes, if applicable). 

6.4.1 Hach CL-17 Free and Total Chlorine 

Analyzer

Hach CL-17 Free and Total Chlorine Analyzers both 

utilize the colorimetric method. Buffer and reagent 

solutions, which adjust the pH and react with chlorine 

to produce a color, are added to the sample. The color 

depth is proportional to the amount of chlorine. Buffer 

and reagent solutions last about a month. The estimated 

cost of the buffer and reagent solution consumables is 

between $750 and $1,000 a year. In addition, the tubing, 

stir-bar, and plastic-tube connectors need to be replaced 

every six months. The colorimeter needs to be cleaned 

every six months. This unit requires no calibration.

6.4.2 Wallace & Tiernan® Depolox® 3 plus

The Wallace & Tiernan® Depolox® 3 plus employs a 

reagent free (potentiostatic 3-electrode-amperometric) 

method for measurement. There is an option for either a 

bare electrode or membrane-type measurement. The option selection is based on water hardness, conductivity, 

and variation in pH. The membrane-type instrument is 

recommended for a higher pH range (6 to 10 pH usable 

range) and low conductivity (10 – 2,500 μS/cm) waters. 

In high pH waters, buffering (e.g., using CO2) is needed. 

Free chlorine consists of chlorine molecules (Cl2), 

hypochlorous acid (HOCl) and hypochlorite ions 

(OCl-). The presence of each component is mostly dependent upon pH with some influence of temperature. 

The HOCl component is the most effective component of free chlorine for disinfection. The electrodes 

typically measure the HOCl component, and report it 

as free chlorine. In membrane-type instruments, the 

membrane is designed to allow only the HOCl acid 

through the membrane, which is measured and reported as free chlorine. At a pH of > 8.5, the majority 

of the HOCl is converted to OCl-, thus, interfering 

with the accurate measurement of free chlorine. From 

a maintenance perspective, the following considerations are noted:

• Replace the electrolyte in the electrode 

reservoir (semi-annually)

• In the bare electrode model, replace the 

grit (that self-cleans the electrode) (semiannually)

• In the membrane type model, clean the 

electrode tip with abrasive paper and replace the 

membrane (every three years)

The cost of consumables ranges between $350 and 

$1,000 a year. The calibration of the bare electrode 

is performed by turning the sample flow off and setting to zero (after waiting several minutes). The water is then turned back on, and after waiting for the 

reading to stabilize, a grab sample is collected, a N, 

N-diethyl-p-phenylenediamine measurement is performed and the span set to match the test result. For 

the membrane-type unit, there is zero calibration.

6.4.3 YSI 6920DW

The YSI 6920DW is a multi-parameter (free chlorine, 

turbidity, temperature, conductivity, pH, and ORP) instrument that uses a reagent-free (amperometric membrane) method for free chlorine measurement (similar 

to the instrument described in Section 6.4.2). From a 

maintenance perspective:

• Replace the electrolyte and membrane 

(quarterly)

The annual cost of consumables is approximately 

$2,100 a year. 

6.4.4 Analytical Technology, Inc., Model 

A15/62 Free Chlorine Monitor

The Analytical Technology, Inc., Model A15/62 free 

chlorine monitor instrument uses a reagent-free (polarographic membrane) method for free chlorine measurement (similar to the instrument described in Section 6.4.2). 
6-4

• Replace the electrolyte and membrane 

(quarterly)

The annual cost of consumables is approximately $100 

a year. The consumable costs for this instrument are 

less expensive when compared to the Wallace & Tiernan® Depolox® 3 plus instrument, because there is no 

pH electrode to be replaced.

6.4.5 Rosemount Analytical Model FCL

The Rosemount Analytical Model FCL uses a reagentfree (membrane type amperometric) method for free 

chlorine measurement (similar to the instrument described in Section 6.4.2). In addition, the Rosemount 

Analytical Model FCL-01 (with manual pH adjustment) chlorine sensor can measure both HOCl and 

OCl- forms of chlorine. The sensor responds differently 

to each form and compensates for both sample pH and 

temperature. From a maintenance perspective:

• Replace the electrolyte and membrane 

(quarterly)

• In addition, the membrane needs to be cleaned 

(monthly)

During the contaminant injections, this membrane appeared to be more prone to fouling than the other membrane-type instruments. The annual cost of consumables is approximately $400 a year. 

6.5 Conductivity Instrumentation

The presence of dissolved mineral substances such as 

chloride, nitrate, sulfate, and phosphate anions (ions 

that carry a negative charge) or sodium, magnesium, 

calcium, iron, and aluminum cations (ions that carry 

a positive charge) dissolved in water can be measured 

as conductivity (or temperature-compensated specific 

conductance). Conductivity is basically a measurement of the sample water’s ability to carry an electrical current. There are several factors that affect the 

conductivity of water including: concentration of ions; 

mobility of ions; oxidation state (valence); and temperature. The testing at the T&E Facility indicated that 

a high volume of contaminant injection was needed 

for the conductivity value to change significantly. 

Conductivity is typically measured directly by either 

measuring the voltage drop or the current flow through 

a sample.

The performance of the conductivity probes was similar 

for the probes tested at the T&E Facility. The probes 

evaluated at the facility included the following: Hach/

GLI Model C53 Conductivity Analyzer, YSI 6600, YSI 

6920DW, Hydrolab® DS5 and Troll® 9000. From a 

maintenance perspective:

• These probes need to be cleaned and calibrated 

(quarterly)

• The probe requires replacement as needed 

(generally lasts at least a year)

The consumables include calibration solutions that cost 

approximately $200 per year.

6.6 pH/ Oxygen Reduction 

Potential Instrumentation

The majority of the manufacturers combine the pH/

ORP measurement with a reference electrode. The pH 

value is a measure of the activity of hydrogen ions (H+) 

in the water sample. Therefore, it a measure of the degree of acidity or alkalinity of the water sample. ORP 

is a measure of the tendency of the water sample to 

oxidize or reduce another chemical substance. Typically, ORP is measured using an inert metal electrode 

(platinum), which will donate electrons to an oxidizing 

agent or accept electrons from a reducing agent. The 

ORP electrode continues to accept or donate electrons 

until it develops a potential that is equal to the ORP of 

the solution. ORP is sometimes utilized for estimating 

the concentration of chlorine in water. However, ORP 

measurement is affected by many factors and might 

not be a good surrogate for chlorine. The testing at the 

T&E Facility indicated that the ORP probes took longer 

than pH probes to return to baseline and grab samples 

for ORP are not reliable. The performance of the pH/

ORP probes was similar for all of the probes tested at 

the T&E Facility. The probes evaluated at the facility 

included the following: Hach/GLI Model P53 pH/ORP 

Analyzer, YSI 6600, YSI 6920DW, Hydrolab® DS5 

and Troll® 9000. From a maintenance perspective:

• These probes need to be cleaned and calibrated 

(quarterly)

• These probes require replacement as needed 

(generally lasts at least a year)

• The Hach/GLI Model P53 pH/ORP Analyzer 

requires annual replacement of the salt-bridge 

and electrolyte solution, which extends the 

instrument life to about 3 years.

The consumables include pH calibration solutions and 

probes that cost approximately $1,000 per year.

6.7 Turbidity

Turbidity is a measure of cloudiness or haziness of the 

water sample caused by the suspended particles. Turbidity is typically determined by shining a light beam of 

wavelengths between 830 and 890 nm into the sample 

solution and then measuring the light (at 90-degrees) 

scattered by the suspended particles. Online nephelo-
6-5

metric turbidity measuring devices were evaluated. The 

turbidity sensors evaluated include the Hydrolab® DS5 

and Hach FilterTrakTM 660 sc Laser Nephelometer. 

From a maintenance perspective:

• The Hach FilterTrakTM 660 sc Laser 

Nephelometer requires quarterly cleaning/

calibration and annual replacement of the light 

source

• Hydrolab® DS5 turbidimeters require semiannual replacement of the wiper

The Hydrolab® DS5 optical turbidimeter probes last 

about three years and cost approximately $1,500 per 

unit. The annualized cost for consumables, including 

the optical probe and calibration solutions, is approximately $1,000.

The annualized consumables cost for the Hach FilterTrakTM 660 sc Laser Nephelometer, including the bulb 

replacement and calibration solution, is approximately 

$300.

6.8 Dissolved Oxygen

Dissolved oxygen probes are used to measure the 

amount of gaseous oxygen (O2) dissolved in the water 

sample. Dissolved oxygen levels did not significantly 

change after the initial contaminant injections and 

therefore was removed from further evaluation.

6.9 Other Conventional 

Water Quality Parameter/

Instrumentation

During the initial phases of testing at the T&E Facility, 

various other ion-selective electrodes (ISEs) were also 

evaluated for their efficacy and usefulness in detecting 

changes in water quality. The other ISEs evaluated for 

their ability to measure the following parameters: ammonia, nitrate, and chloride. Although some of these 

parameters changed in the presence of the contaminants, free chlorine interfered with ISE calibration and 

prevented the repeatability of the measurement. In addition, some of the ISEs burned out too quickly and were 

expensive to replace. Therefore, these parameters were 

excluded from further testing.

6.10 Online Optical 

Instrumentation

The following optical instruments (listed alphabetically) were evaluated mostly for their ability to detect 

biological agents and growth media at the T&E Facility: FlowCAM®, Hach 2200 PCX Particle Counter, Hach FilterTrakTM 660 sc Laser Nephelometer, 

BioSentry®, Spectro::lyzerTM and Carbo::lyzerTM and 

ZAPS MP-1. In addition, because of their ability to 

detect contaminants in addition to biological organisms, the Spectro::lyzerTM and Carbo::lyzerTM and the 

ZAPS MP-1 were tested using various chemical injections. Generally, the biological organism tests were performed by injecting 100, 600, 1,000, and 25,000 cells/

mL (both in chlorinated and dechlorinated waters). 

Based on previous testing, EPA had discovered that the 

online chlorine and TOC monitors generated reliable 

responses at injections of 100,000 cells/mL and some 

responses at 25,000 cells/mL, but the response faded 

below this level. 

Generally, these optical instruments were evaluated as 

they became available for the research and, therefore, 

evaluations utilizing all contaminants with each instrument were not performed. The following is a brief summary of their capability and equipment performance 

observation.

6.10.1 FlowCAM®

FlowCAM® is an online particle imaging and flowcytometry system that takes high-resolution digital 

images of particles and cells in the water sample. The 

images are analyzed by a proprietary software program 

based on Microsoft® Office Excel® that captures and 

analyzes the parameters of the particles such as count, 

size, length, shape, and equivalent spherical diameter. 

In addition, the instrument captures the intensity, transparency, color, bio-volume, compactness, roughness, 

and elongations of the particles. 

At the T&E Facility, Ankistodesmus (20 to 100 micrometers (μm)), Selenestrum (10 μm), and Saccharomyces yeast (1.5 μm) were injected. The unit was 

able to recognize all of the particles. In order to capture individual images, the flow cytometry setup significantly reduces the flow volume to the unit. This 

restrictive flow path causes significant delays in the 

measurement from the injection time. At concentrations below 1,000 cells/mL, the instrument is unable 

to differentiate baseline noise from injected contaminants. Sub-micron particles such as E. coli (0.8 to 0.9 

μm), bacteriophage MS2 (0.02 to 0.03 μm) and B. globigii (0.5 to 0.9 μm – spores are smaller than the cells) 

were not identifiable with the existing camera optical 

resolution and flow cell. However, at sufficiently high 

concentrations, the instrument is able to show an increased count.

6.10.2 Hach FilterTrakTM 660 sc Laser 

Nephelometer and Hach 2200 PCX 

Particle Counter

The Hach FilterTrakTM 660 laser nephelometer uses 

a collimated light source with high beam density 

and a distinct wavelength to detect baseline turbid-
6-6

ity change as low as 0.5 milli-nephelometric turbidity 

units (mNTU) (0.0005 NTU). The instrument is optimized to detect particles in the 0.1 to 0.5 μm range. 

The Hach 2200 PCX Particle Counter is a laser-diode-based particle counter designed for drinking water applications. The instrument is optimized to detect 

particles in the range of 2-750 μm.

The instruments were challenged with the following 

biological injections performed at the T&E Facility: 

E. coli, MS2 and B. globigii. The Hach Filter/TrakTM 

660 sc Laser Nephelometer was able to detect only E. 

coli and B. globigii at injection levels of 25,000 cells/

mL. The Hach 2200 PCX Particle Counter was unable to detect any of the biological injections at these 

levels. The particle counter is designed for detecting 

larger biological particles such as Cryptosporidium 

spp. and Giardia lamblia.

6.10.3 BioSentry® 

The BioSentry® system is a laser-based, continuous, online, real-time monitoring device for detecting 

microorganisms in water. The unit utilizes laser-produced, multi-angle light scattering (MALS) technology to generate unique bio-optical signatures for 

classification using BioSentry®’s pathogen detection 

library. BioSentry® can be set up to detect microorganisms and identify suspected pathogens. 

The biological injections performed at the T&E Facility included the following: 3-micron beads (surrogates for Cryptosporidium spp.), B. globigii and E. 

coli. In the current design (as tested), the unit has to 

be programmed to identify a specific contaminant, 

whereas all others (even when detected) are identified 

as unknowns. Prior to any injection(s), the unit should 

be programmed to recognize the injected particle, i.e., 

the unique bio-optical MALS signature should be developed and put to use (using the local water and pure 

form of the injected particle). The unit tested at the 

T&E Facility was able to reliably recognize injection 

events and identify injected particles between 1,000 

and 10,000 cells/mL level. At lower cell concentrations, the detection was not consistent across the test 

runs.

6.10.4 Spectro::lyzerTM/Carbo::lyzerTM

These units are based on UV-Vis spectroscopic absorption measurements. Contaminants that respond to the 

UV-Vis absorption spectra can be detected by this instrument. The Spectro::lyserTM was programmed to 

measure the optical equivalents of turbidity, nitrate, 

TOC, and dissolved organic carbon (DOC). In addition, 

the Spectro::lyzerTM was connected to the con::statTM 

process control terminal, which ran the proprietary software to compute four pre-set alarm parameters based 

on computations of spectral channels that were considered to be important by the manufacturer. 

At the T&E Facility, both chemical and biological injections were performed using the Single Pass DSS. 

The chemical injections included: humic acid, sodium 

fluoroacetate, aldicarb, dicamba, and gasoline. The instrument was able to detect the water quality changes 

for all of the contaminants, except sodium fluoroacetate. The biological injections included the following: 

sucrose, E. coli and B. globigii. The instrument was 

able to detect changes at injection levels of approximately 25,000 cells/mL.

Results at the T&E Facility indicated that this device 

was capable of serving as a good surrogate for traditional 

TOC measurement. The operation and maintenance requirements for this device were minimal when compared 

to the traditional UV-persulfate method-based TOC 

measuring devices. Also, the size of this device is much 

smaller than the traditional TOC measuring devices. As 

mentioned previously in Chapters 2, 3, and 5, TOC is 

a critical water quality trigger parameter. EPA recommends the use of this type of device especially at locations where the traditional TOC devices are difficult to 

deploy, either due to size or due to ongoing operational 

and maintenance costs. Subsequently, the Carbo::lyzerTM 

was successfully deployed at two locations under the 

WSi pilot study in Cincinnati alongside the traditional 

TOC measuring devices. Furthermore, this instrument 

and associated software are capable of analyzing the full 

spectrum of UV-Vis absorbance, which if fully exploited, 

can yield additional information about the water quality 

changes that are not captured by the other devices evaluated by EPA at the T&E Facility.

6.10.5 ZAPS MP-1

The ZAPS MP-1 is an online water quality monitoring device that can be programmed to measure up to 

100 slices of optical wavelengths (using optical filters) 

between 200 and 800 nanometers. The optical data that 

can be captured includes absorption, fluorescence, and 

total reflection bands. The ZAPS MP-1 configuration 

at the T&E Facility was set up to measure the following parameters: dark counts, pinhole, nitrate, ultraviolet 

254 nanometer wavelength (UV254) absorbance, bacterial fluorescence, humic fluorescence, total fluorescence, rhodamine, and transmission. The individual excitation and response wavelengths were pre-set by the 

manufacturer to these parameters.

At the T&E Facility, nitrate, formazin, Saccharomyces 

cerevisiae (yeast) and E. coli were injected to evaluate 

responses on this instrument. The instrument responded well to nitrate injections at 0.14 mg/L. The instrument responded well to the injection of formazin (tur-
6-7

bidity standard) at test levels of 13.3 NTU and 26.6 

NTU; a linear response was observed in UV254, total 

fluorescence, bacterial fluorescence, and nitrate channels. However, for Saccharomyces cerevisiae (yeast) 

and E. coli injections, the instrument showed no response below 100,000 cells/mL. Therefore, no further 

testing was performed with the biological agents.

6.11 Best Practices and Lessons 

Learned

As presented in Sections 6.1 through 6.9, many conventional instruments are relatively easy to operate 

and maintain. However, the TOC instruments are 

more complex and require a relatively higher level of 

technical skill. A formal training class provided by 

the manufacturer for each type of instrument is highly 

recommended. Just by following the instruction manuals, experienced instrumentation technicians and 

field engineers who have several years of experience 

are usually capable of installing, setting up, calibrating, and operating new or unfamiliar instrumentation. 

Less experienced staff will require assistance from the 

instrument manufacturer to avoid some of the pitfalls 

that can cause serious damage to an instrument and 

result in improper/inefficient operation. For example, 

the Sievers® 900 On-Line TOC Analyzer requires the 

de-ionized loop reservoir to be filled prior to power 

up; otherwise, air is trapped in the measurement modules, which will require factory service. Furthermore, 

factory default settings for an instrument might not be 

suitable for the location; the operator should be aware 

of the exact settings for each location prior to service. 

As another example, when installing a probe to an 

analyzer such as Wallace & Tiernan® Depolox® 3 

plus, it is necessary to complete a formal setup in the 

instrument analyzer to recognize the probe correctly. 

It is always advisable to attend training workshops 

offered by most instrument manufacturers. Adequate 

staff training for setup and maintenance activities is 

essential for optimal operations. In addition, for more 

complex instruments (such as the optical instrumentation), it is essential for the maintenance technician 

to receive formal manufacturer training.

The operation of a single or a few instruments is a 

relatively straightforward process; however, the operation of a multi-station network or multiple networks 

can be a logistical nightmare. Loss of data, false 

alarms, and other malfunctions can lead to improper 

analysis of data by algorithms and inappropriate actions. The operator should consult with the instrument 

manufacturer(s) and develop a thorough understanding of the instrument outputs during power outages 

and other malfunctions such as the loss of reagents. It 

is important to develop a monitoring plan for scheduled maintenance, order expendable supplies in a 

timely manner, maintain calibration standards, and 

schedule sufficient manpower to cover network operations. Following a good monitoring plan will ensure the collection of high-quality data that meets the 

monitoring requirements.

In general, most instrument problems are related to 

flow (sample and/or reagent issues). The sample flow 

problem could be related to restrictions in the flow 

manifold or restriction of flow through the instrument. 

Reagent flow blockage can result in diminished or unstable readings. If the reagents run out completely, the 

readings typically drop to zero and are easy to spot. 

Some instruments are factory-set to hold the last good 

reading; if the numbers do not change over a significant period, it is an indication of instrument failure. 

For membrane-type probes, the reading usually drifts 

downwards as the membrane is clogged or nearing its 

useful life. For electrodes, failure is generally indicated by problems during calibration where either the 

slope or the gain or the cell constant is outside of the 

manufacturer-recommended tolerance range.

Water Utilities and

Sensor Manufacturers

• Over the life of the equipment operation, in general, 

the O&M cost will exceed capital cost for most 

online sensing equipment. At a minimum, the 

water utilities should set aside a budget for annual 

labor and consumable costs, based on the detailed 

information presented in this chapter.

• The evaluations at the EPA T&E Facility and 

during the WSi pilot study revealed that almost 

60% to 80% of the O&M labor cost associated with 

the water quality monitoring sensors was related to 

TOC instruments.

• Instrument technicians should be appropriately 

factory-trained for optimal operations.

• O&M activities should be appropriately scheduled. 

Consumables should be purchased in a timely 

manner so that they do not expire before they can 

be used up.

• Ideally, sensor manufacturers need to develop 

reagent-free sensors that result in lower labor and 

consumable costs.

• In the absence of better TOC instrumentation, TOC 

surrogate monitoring (such as the S::CAN – see 

section 6.10.4) should be seriously considered for 

both chlorinated and chloraminated systems as the 

maintenance requirement for this type of device is 

minimal.
6-8

Degassing or bubbles can cause improper readings. 

Degassing is generally an issue where the incoming 

sample water is colder than the environmental housing 

of the instrument.

When purchasing consumables, one needs to be sure to 

use them prior to their expiration dates. For some ISEs, 

the shelf life begins from the date of manufacture and 

not the date of installation. When planning to purchase 

spare parts and consumables, the shelf life and projected use by date should be taken into consideration. Water 

utilities are encouraged to work with manufacturers to 

negotiate purchase price of equipment based on volume 

of purchase and any negotiated long-term service contracts.
7-1

7.0 Bibliography

The references included in this bibliography contain 

additional detailed information for readers who wish to 

pursue, in greater detail, the specific topics discussed 

in this guide. Many of these references (especially the 

EPA references) are freely available on the Internet. 

The references are listed alphabetically, based on the 

last name of the first author(s). In cases where there 

are two or more works by the same author (e.g., EPA), 

the entries are listed chronologically.

Allgeier, S.C., J. Pulz, and R. Murray, 2006. Conceptual design of a contamination warning system. 

Proceedings of the AWWA Water Security Congress, 

Washington, DC.

AwwaRF, 2002. Online monitoring for drinking water 

utilities. Editor, Erika Hargesheimer, Awwa Research 

Foundation and CRS PROAQUA, American Water 

Works Association, Denver, CO.

Dawsey, W.J., B.S. Minsker, and V.L. VanBlaricum, 

2006. Bayesian belief networks to integrate monitoring 

evidence of water distribution system contamination. 

Journal of Water Resources and Planning, ASCE, 

132:4:234.

Department of Homeland Security, 2006. National 

Infrastructure Protection Plan.

Edberg, S. C., E.W. Rice, R. J. Karlin, and M. J. Allen, 

2000. Escherichia coli: The Best Biological Drinking 

Water Indicator for Public Health Protection. Journal 

of Applied Microbiology, 88 (S6).

Einfeld, W., S. McKenna, and M.P. Wilson, 2008. 

A Simulation Tool to Assess Contaminant Warning 

System Sensor Performance Characteristics. AwwaRF, 

Denver, CO.

EPA, 2003. Contamination threat management guide - 

Module 2. EPA/817/D-03/002, Washington, DC.

EPA, 2004a. The Water Security Research and Technical Support Action Plan. EPA/600/R-04/063, Washington, DC.

EPA, 2004b. Public Drinking Water Systems Programs: http://www.epa.gov/safewater/pws/pwss.html.

EPA, 2004c. Response Protocol Toolbox Overview. 

EPA/817/D-03/007, Washington, DC.

EPA, 2004d. Response Protocol Toolbox: Water Utility Planning Guide - Module 1. EPA/817/D-03/001, 

Washington, DC.

EPA, 2004e. Response Protocol Toolbox: Contamination Threat Management Guide - Module 2. 

EPA/817/D-03/002, Washington, DC.

EPA, 2004f. Response Protocol Toolbox: Site 

Characterization and Sampling Guide - Module 3. 

EPA/817/D-03/003, Washington, DC.

EPA, 2004g. Response Protocol Toolbox: Analytical 

Guide - Module 4. EPA/817/D-03/004, Washington, 

DC.

EPA, 2004h. Response Protocol Toolbox: 

Public Health Response Guide - Module 5. 

EPA/817/D-03/005, Washington, DC.

EPA, 2004i. Response Protocol Toolbox: Remediation 

and Recovery Guide - Module 6. EPA/817/D-03/006, 

Washington, DC.

EPA, 2004j. Response Protocol Toolbox: Planning 

for and Responding to Drinking Water Contamination Threats and Incidents Response Guidelines. 

EPA/817/D-04/001, Washington, DC.

EPA, 2004k. Homeland Security Strategy. Washington, 

DC.

EPA, 2005a. Evaluation of Water Quality Sensors as 

Devices to Warn of Intentional Contamination in Water 

Distribution Systems. EPA/600/R-05/105, Washington, 

DC. Available only through WaterISAC.

EPA, 2005b. WaterSentinel Online Water Quality 

Monitoring as an Indicator of Drinking Water Contamination. EPA/817/D-05/002, Washington, DC. 

EPA, 2005c. WaterSentinel System Architecture. 

EPA/817/D-05/003, Washington, DC.

EPA, 2005d. Technologies and Techniques for Early 

Warning Systems to Monitor and Evaluate Drinking Water Quality: A State-of-the-Art Review. 

EPA/600/R-05/156, Washington, DC.

EPA, 2007a. Water Quality Sensor Response to Contamination in a Single Pass Water Distribution System 

Simulator. EPA/600/R-07/001, Washington, DC. 

Available only through WaterISAC.
7-2

EPA, 2007b. Water Security Initiative: Interim Guidance on Planning for Contamination Warning System 

Deployment. EPA/817/R-07/002, Washington, DC.

EPA, 2008a. Water Security Initiative: Interim Guidance on Developing an Operational Strategy for 

Contamination Warning Systems. EPA/817/R-08/002, 

Washington, DC.

EPA, 2008b. Water Security Initiative: Interim Guidance on Developing Consequence Management Plans 

for Drinking Water Utilities. EPA/817/R-08/001, 

Washington, DC.

EPA, 2008c. Water Security Initiative: Cincinnati Pilot 

Post-Implementation System Status - Covering the 

Pilot Period: December 2005 through December 2007. 

EPA/817/R-08/004, Washington, DC.

Hall, J.S., A.D. Zaffiro, R.B. Marx, P.C. Kefauver, 

E.R. Krishnan, R.C. Haught, and J.G. Herrmann, 

2007. On-line water quality parameters as indicators of 

distribution system contamination. Journal of American Water Works Association, 99:1:66.

Homeland Security Presidential Directive-7/HSPD-7, 

2003. Critical Infrastructure Identification, Prioritization, and Protection: http://www.dhs.gov/xabout/laws/

gc_1214597989952.shtm.

Homeland Security Presidential Directive-9/HSPD-9, 

2004. Defense of United States Agriculture and Food: 

http://www.dhs.gov/xabout/laws/gc_1217449547663.

shtm.

ISLI, 1999. Early warning monitoring to detect hazardous events in water supplies. ISLI Press, Washington, DC.

Kessler, A., A. Ostfeld, and G. Sinai, 1998. Detecting accidental contaminations in municipal water 

networks. Journal of Water Resources Planning and 

Management-ASCE, 124:4:192.

Kirmeyer, G., M. Friedman, and K. Martel, 2002. Guidance manual for monitoring distribution system water 

quality. Awwa Research Foundation, Denver, CO.

Kroll, D., and K. King, 2007. To switch or not to 

switch. Potential security considerations in the free 

chlorine versus monochloramine debate for drinking 

water disinfection. Proceedings of the ASM Biodefense and Emerging Diseases Research Meeting, 

Washington, DC.

Lytle, J., and E.W. Rice, 2002. A Systematic Comparison of the Electrokinetic Properties of Environmentally Important Microorganisms in Water. Colloids and 

Surfaces B: Biointerfaces, 24, 91-101.

McKenna, S.A., K.A Klise, and M.P. Wilson, 2006. 

Testing water quality change detection algorithms. 

Proceedings of the 8th Annual Water Distribution Systems Analysis (WDSA) Symposium, Cincinnati, OH.

O’Halloran, R., R. Jarrett, G. Robinson, and P. Toscas, 

2009. Data Processing and Analysis for Online Distribution System Monitoring, AwwaRF, Denver, CO.

Rice, E. W., N. J. Adcock, M. Sivaganesan, and L. J. 

Rose, 2005. Inactivation of spores of Bacillus anthracis Sterne, Bacillus cereus, and Bacillus thuringiensis 

subsp. israelensis by chlorination. Applied and Environmental Microbiology. 9: 5587-5589.

Roberson, J.A. and K.M. Morley, 2005, Guidance 

document, contamination warning systems for water: 

An approach for providing actionable information to 

decision-makers, AWWA.

Sivaganesan, M., N.J. Adcock, and E.W. Rice, 2006. 

Inactivation of Bacillus globigii by chlorination: A 

Bayesian model. Journal of Water Supply Research 

Technology - Aqua, 55.1: 33-43.

Szabo, J.G., J.S. Hall, and G.C. Meiners, 2006. Water 

quality sensor responses to injected contaminants in a 

chloraminated pipe loop. Proceedings of the AWWA 

Water Security Congress, Washington, DC.

Szabo, J.G., J.S. Hall, and G.C. Meiners, 2008a. Sensor responses to contamination in chloraminated drinking water. Journal of American Water Works Association, April 2008, Denver, CO.

Szabo, J.G., J.S. Hall, and G.C. Meiners, 2008b. 

Detection of biological suspensions using online 

detectors in a model drinking water distribution system 

simulator. AWWA Water Security Congress, Cincinnati, OH.

Umberg, K., N. Mix, B. Pickard, and C. Dangel, 

2009. Event Detection System Evaluation, Selection, 

and Optimization. AWWA Water Security Congress, 

Washington, DC.

Recycled/Recyclable

Printed with vegetable-based ink on

paper that contains a minimum of

50% post-consumer fiber content

processed chlorine free