distribution system water quality control demonstration

Distribution System Water Quality Control Demonstration

Tailored Collaboration

Subject Area: Water Quality

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Jointly sponsored by: Water Research Foundation 6666 West Quincy Avenue, Denver, CO 80235-3098

and

North Shore Water Commission 400 West Bender Road, Glendale, WI 53217

Published by:

Prepared by: Abigail F. Cantor Process Research Solutions, LLC, Madison, WI

Eric Kiefer North Shore Water Commission, Glendale, WI

Kevin Little Informing Ecological Design, LLC, Madison, WI

Andrew Jacque University of Wisconsin-Platteville Platteville, WI

Archie Degnan Wisconsin State Laboratory of Hygiene, Madison, WI

Barry Maynard and David Mast University of Cincinnati, Cincinnati, OH

and

Judith Cantor Acer Enterprises, Inc. Raleigh, NC


DISCLAIMER

This study was jointly funded by the Water Research Foundation (Foundation) and the North Shore Water Commission (NSWC). The Foundation and NSWC assume no responsibility for the content of the

research study reported in this publication or for the opinions or statements of fact expressed in the report. The mention of trade names for commercial products does not represent or imply the approval or endorsement of the Foundation or NSWC. This report is presented solely for informational purposes.

Copyright © 2012by Water Research Foundation

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

or otherwise utilized without permission.

ISBN 978-1-60573-174-2

Printed in the U.S.A.


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CONTENTS

TABLES ........................................................................................................................................ ix FIGURES ....................................................................................................................................... xi FOREWORD .............................................................................................................................. xvii ACKNOWLEDGMENTS ........................................................................................................... xix EXECUTIVE SUMMARY ......................................................................................................... xxi

CHAPTER 1 INTRODUCTION .................................................................................................... 1 Basic Water System Process Control .................................................................................. 1 Water Quality Efforts Motivated by Regulatory Compliance and Consumer Complaints 2 Current Focus of Water System Process Control ............................................................... 2 Current Efforts to Change Water System Process Control Perspectives ............................ 4 A Focus on Product Quality in a Water System ................................................................. 4 A Comprehensive, Consumer-centric Process Control Methodology for Drinking Water

Systems ................................................................................................................... 5 Objectives of This Study ..................................................................................................... 5

CHAPTER 2 METHODS AND MATERIALS ............................................................................. 9

Water System ...................................................................................................................... 9 Tool #1: Strategy of Monitoring ........................................................................................ 9

Representative Water Quality Parameters .............................................................. 9 Field and Utility Laboratory Analyses .................................................................. 15 Laboratory Analyses ............................................................................................. 21

Tool #2: Capture of Water Quality Experienced By the Consumer ................................ 21 Representative Water Distribution System Samples ............................................ 21 Importance of Metal Transfer in Comprehensive Process Control and Vice

Versa ......................................................................................................... 23 PRS Monitoring Stations ...................................................................................... 23

PRS Monitoring Station Startup ........................................................................... 24 PRS Monitoring Station Operation ....................................................................... 24 Tool #3: Interpretation of Water System Data ................................................................. 27

Shewhart Control Charts ....................................................................................... 27 Project Data Analyses ........................................................................................... 32

Additional Information ..................................................................................................... 32 Data from Previous Distribution System Monitoring Projects ............................. 33 Examination of Metal Plates ................................................................................. 33 Residential Sampling ............................................................................................ 34 Lead and Copper Rule Sampling .......................................................................... 34 SCADA Data ........................................................................................................ 35


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On-line Sensor Data .............................................................................................. 35 Regulatory and Investigative Data ........................................................................ 35

CHAPTER 3 RESULTS ............................................................................................................... 37

Monitoring Station Data ................................................................................................... 37 Lead, Copper, and Microbiological Activity ........................................................ 38

Influent Conditions ............................................................................................... 39 Correlation of Influent Conditions on Lead, Copper, and Microbiological Activity ..................................................................................................... 65 Data from Previous Monitoring Station and Pipe Loop Projects ......................... 74

Residential Data ................................................................................................................ 80 Lead and Copper Rule Data .............................................................................................. 87 Metal Plate Microbiological Analysis .............................................................................. 87 Metal Plate Chemical Analysis ......................................................................................... 89 SCADA Data .................................................................................................................... 90 Online Sensor Data ........................................................................................................... 91 Regulatory and Investigative Data .................................................................................... 96

CHAPTER 4 DISCUSSION AND CONCLUSIONS ................................................................ 101

Representative Distribution System Water Samples ...................................................... 103 Shewhart Control Charts ................................................................................................. 107 Example of an Iterative Process Control Methodology .................................................. 108 Summary of a Comprehensive Consumer-centric Process Control Methodology ......... 110

Distribution System Process Control: Delivered water quality versus entry point water quality.............................................................................................110

Water Treatment Process Control: Delivered water quality versus water treatment parameters ............................................................................................... 111

Water Treatment Process Control: Delivered water quality versus source water parameters ............................................................................................... 112

Comprehensive Perspective on Process Control................................................. 112 CHAPTER 5 GETTING STARTED WITH COMPREHENSIVE CONSUMER-CENTRIC PROCESS CONTROL ............................................................................................................... 113

Getting Started with Shewhart Control Charts ............................................................... 113 Getting Started with a Comprehensive Consumer-centric Process Control Methodology ....................................................................................................... 114

CHAPTER 6 SUMMARY OF BENEFITS ................................................................................ 117

System Operation ............................................................................................................ 117 Triggering the need to troubleshoot equipment or other system operations ....... 117 Saving money on treatment chemicals ............................................................... 117 Minimizing temporary water quality degradation .............................................. 117 Evaluating system operations routinely .............................................................. 118

Lead and Copper Rule Issues .......................................................................................... 118 Addressing Lead and Copper Rule compliance issues scientifically .................. 118

Beyond Lead and Copper Rule Issues ............................................................................ 119


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Assessing and controlling biostability of water, aiding in setting an appropriate disinfection concentration, and staying in compliance with the Total Coliform Rule ......................................................................................... 119

Assessing cleanliness of the piping system ........................................................ 119 Developing hypotheses for further research on a system level and on a national

level ......................................................................................................... 119 A Comprehensive Consumer-Centric Process Control Methodology ............................ 119

Establishing a water quality control methodology .............................................. 119 Establishing a water system process improvement methodology ....................... 120

APPENDIX A MICROBIOLOGICAL GROWTH INDICATOR ............................................ 121

Description ...................................................................................................................... 121 Procedure ........................................................................................................................ 121 Results ............................................................................................................................. 122

APPENDIX B METAL PLATE BIOFILM ANALYSIS .......................................................... 129

Description ...................................................................................................................... 129 procedure ......................................................................................................................... 129 Results ............................................................................................................................. 130

APPENDIX C MICROBIOLOGICAL INSPECTION OF METAL PLATES .......................... 133


APPENDIX D METALLURGICAL INSPECTION OF METAL PLATES ............................ 151


REFERENCES ........................................................................................................................... 167


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TABLES

Table 2.1 NSWC analytical bias summary ................................................................................... 17 Table 2.2 NSWC analytical precision based on Shewhart control chart estimates of variation ... 21 Table 2.3 NSWC sampling sites on two PRS Monitoring Stations .............................................. 26 Table 2.4 Example calculation for an Individuals Chart (Subgroup Size One) ............................ 30 Table 3.1 Phases of orthophosphate dosage changes .................................................................... 37 Table 3.2 NSWC general water quality parameters ...................................................................... 45 Table 3.3 NSWC change in parameters during stagnation: Entry Point Lead Test Chamber ...... 53 Table 3.4 NSWC change in parameters during stagnation: Entry Point Copper Test Chamber .. 54 Table 3.5 NSWC change in parameters during stagnation: High Water Age Lead Test Chamber ............................................................................................................................ 55 Table 3.6 NSWC change in parameters during stagnation: High Water Age Copper Test

Chamber ............................................................................................................................ 56 Table 3.7 NSWC assimilable organic carbon (µg/L as acetate-C) ............................................... 57 Table 3.8 Correlation matrix: Entry Point Monitoring Station Lead Test Chamber .................... 66 Table 3.9 Correlation matrix: High Water Age Monitoring Station Lead Test Chamber ............ 68 Table 3.10 Correlation matrix: Entry Point Monitoring Station Copper Test Chamber .............. 70 Table 3.11 Correlation matrix: Entry Point Monitoring Station Copper Test Chamber .............. 72 Table 3.12 Linear correlation of influent parameters and lead and copper concentrations .......... 75 Table 3.13 Linear correlation of influent parameters and microbiological activity ..................... 76 Table 3.14 Linear correlation between influent parameters ......................................................... 77 Table 3.15 Residential sampling in NSWC distribution systems ................................................. 81 Table 3.16 Microbiological tests performed on PRS Monitoring Station metal plates ................ 89 Table 3.17 Compounds found on PRS Monitoring Station metal plates ...................................... 89


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Table B.1 Heterotrophic plate counts (HPC) .............................................................................. 130 Table B.2 Iron bacteria counts .................................................................................................... 130 Table B.3 Sulfate-reducing bacteria (SRB; presence / absence) ................................................ 131 Table D.1 Scale chemistry by X-Ray Fluorescence ................................................................... 162 Table D.2 X-Ray Diffraction intensities, normalized to underlying metal ................................. 162


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FIGURES

Figure 1.1 A comprehensive consumer-centric process control method for drinking water systems ................................................................................................................................ 7

Figure 2.1 Schematic of water treatment at North Shore Water Commission .............................. 10 Figure 2.2 NSWC analytical measurement standards ................................................................... 16 Figure 2.3 NSWC analytical measurement duplicates ................................................................. 17 Figure 2.4 Photo of a 2011 PRS Monitoring Station .................................................................... 25 Figure 2.5 Adaptation of PRS Monitoring Station design at NSWC ........................................... 26 Figure 2.6 Basic features of a Shewhart control chart .................................................................. 27 Figure 2.7 Individuals Chart and associated Range Chart from Table 2.4 data ........................... 31 Figure 2.8 Removal of biofilms from PRS Monitoring Station metal plates ............................... 34 Figure 3.1 Control charts: monitoring stations’ lead concentrations ............................................ 40 Figure 3.2 Summary charts: monitoring stations’ lead concentrations ......................................... 41 Figure 3.3 Control charts: monitoring stations’ copper concentrations ........................................ 42 Figure 3.4 Summary charts: monitoring stations’ copper concentrations .................................... 43 Figure 3.5 Control charts: monitoring stations’ HPC concentrations ........................................... 44 Figure 3.6 Summary charts: monitoring stations’ HPC concentrations ....................................... 45 Figure 3.7 Control charts: influent pH and temperature ............................................................... 48 Figure 3.8 Summary charts: influent pH and temperature ............................................................ 49 Figure 3.9 Control charts: influent disinfection ............................................................................ 50 Figure 3.10 Summary charts: influent disinfection ....................................................................... 51 Figure 3.11 Control charts: influent free ammonia ....................................................................... 52 Figure 3.12 Summary charts: influent free ammonia .................................................................. 52 Figure 3.13 Control charts: influent orthophosphate .................................................................... 57


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Figure 3.14 Summary charts: influent orthophosphate ................................................................. 57 Figure 3.15 Control charts: influent aluminum and sulfate .......................................................... 59 Figure 3.16 Summary charts: influent aluminum and sulfate ....................................................... 60 Figure 3.17 Control charts: influent chloride and CSMR ............................................................. 61 Figure 3.18 Summary charts: influent chloride and CSMR ......................................................... 62 Figure 3.19 Control charts: influent iron and turbidity ................................................................. 63 Figure 3.20 Summary charts: influent iron and turbidity ............................................................. 64 Figure 3.21 Scatterplot matrix: Entry Point Monitoring Station Lead Test Chamber .................. 67 Figure 3.22 Scatterplot matrix: High Water Age Monitoring Station Lead Test Chamber .......... 69 Figure 3.23 Scatterplot matrix: Entry Point Monitoring Station Copper Test Chamber .............. 71 Figure 3.24 Scatterplot matrix: High Water Age Monitoring Station Copper Test Chamber ...... 73 Figure 3.25 NSWC 2008/2009 monitoring during chemical addition changes ............................ 78 Figure 3.26 Onalaska Water Utility 2009/2010 monitoring ......................................................... 78 Figure 3.27 Waukesha Water Utility 2007 and 2009/2010 monitoring........................................ 79 Figure 3.28 Residential sampling in NSWC distribution systems: orthophosphate, turbidity, total

iron .................................................................................................................................... 82 Figure 3.29 Residential sampling in NSWC distribution systems: total chlorine,

monochloramine, free ammonia ....................................................................................... 83 Figure 3.30 Residential sampling in NSWC distribution systems: pH and temperature .............. 84 Figure 3.31 Residential sampling in NSWC distribution systems: chloride and sulfate .............. 85 Figure 3.32 Residential sampling in NSWC distribution systems: lead, copper, HPC ................ 86 Figure 3.33 NSWC Lead and Copper Rule sampling results ....................................................... 88 Figure 3.34 Shewhart control charts used for physical data ......................................................... 92 Figure 3.35 Shewhart control charts used for other chemical feed data ....................................... 93


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Figure 3.36 Shewhart control charts used for disinfection chemical feed data ............................ 94 Figure 3.37 Shewhart control charts used for on-line sensor data ................................................ 95 Figure 3.38 Shewhart control charts used for regulatory distribution system disinfection data:

Part I .................................................................................................................................. 97 Figure 3.39 Shewhart control charts used for regulatory distribution system disinfection data:

Part II ............................................................................................................................... 98 Figure 3.40 Shewhart control charts used for extra distribution system data taken during Total Coliform Rule compliance sampling: Part I ..................................................................... 99 Figure 3.41 Shewhart control charts used for extra distribution system data taken during Total

Coliform Rule compliance sampling: Part II .................................................................. 100 Figure 4.1 Tools and concepts introduced in this study .............................................................. 101 Figure 4.2 A comprehensive consumer-centric process control method for drinking water

systems ............................................................................................................................ 102 Figure A.1 Results for unfiltered MGI assay ............................................................................. 123 Figure A.2 Results for unfiltered PMS assay ............................................................................. 123 Figure A.3 Results for filtered MGI assay ................................................................................. 124 Figure A.4 Results for comparison of MGI assay with filtered MGI assay .............................. 125 Figure A.5 Results for filtered PMS assay................................................................................. 125 Figure A.6 Results for comparison of PMS assay with filtered PMS assay .............................. 126 Figure A.7 Results for MGI assay on 2.5” square plates ........................................................... 127 Figure A.8 Results for PMS assay on 2.5” square plates ........................................................... 127 Figure C.1 SEM of lead plate under spacer with 50X magnification ......................................... 135 Figure C.2 SEM of boxed region shown in Figure C.1 with 5000X magnification ................... 136 Figure C.3 SEM of boxed region shown in Figure C.1 with 5000X magnification ................... 137 Figure C.4 SEM of lead plate under spacer with 500X magnification ....................................... 138 Figure C.5 SEM of upper boxed region shown in Figure C.4 with 2000X magnification ......... 139


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Figure C.6 SEM of lower boxed region shown in Figure C.4 with 5000X magnification ......... 140 Figure C.7 SEM of lead plate exposed to water flow with low magnification ........................... 141 Figure C.8 SEM of boxed region shown in Figure C.7 with 5000X magnification ................... 142 Figure C.9 SEM of copper plate under spacer with 5000X magnification ................................. 143 Figure C.10 SEM of copper plate under spacer with 5000X magnification ............................... 144 Figure C.11 SEM of copper plate under spacer with 50X magnification ................................... 145 Figure C.12 SEM of Boxed Region Shown in Figure C.11 with 500X magnification .............. 146 Figure C.13 SEM of copper plate under spacer .......................................................................... 147 Figure C.14 SEM of copper plate under spacer .......................................................................... 148 Figure C.15 SEM of copper plate under spacer .......................................................................... 149 Figure C.16 SEM of lead plate under spacer with 10000X magnification ................................. 150 Figure D.1 General appearance of PRS Monitoring Station lead and copper plates after one year

exposure to NSWC water................................................................................................ 152 Figure D.2 SEM analysis of Plate 1-Pb-1. ................................................................................. 153 Figure D.3 SEM analysis of Plate 1-Pb-1. ................................................................................. 154 Figure D.4 SEM analysis of Plate 1-Pb-1. ................................................................................. 155 Figure D.5 SEM Analysis of Plate 2-Pb-1. ................................................................................ 156 Figure D.6 SEM Analysis of Plate 2-Pb-1. ................................................................................ 157 Figure D.7. SEM Analysis of Plate 2-Pb-1. ............................................................................... 158 Figure D.8 SEM Analysis of Plate 2-Pb-1. ................................................................................ 159 Figure D.9 SEM analysis of Plate 1-Cu-1. ................................................................................ 160 Figure D.10 SEM analysis of Plate 2-Cu-1. .............................................................................. 161 Figure D.11 Raman spectrum of lead carbonate observed on lead plate 1-Pb-1 ........................ 163 Figure D.12 Raman spectrum of a cerussite standard ................................................................. 163


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Figure D.13 Raman spectrum of dark red mineral identified as litharge .................................... 164 Figure D.14 Raman spectrum from plate set 2-Pb-1 showing the photo-transformed lead (IV)

oxide peak around 300 cm-1 ........................................................................................... 165 Figure D.15 Raman spectrum of photo-transformed pure lead (IV) oxide ................................. 165


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FOREWORD

The Water Research Foundation (Foundation) is a nonprofit corporation dedicated to the development and implementation of scientifically sound research designed to help drinking water utilities respond to regulatory requirements and address high-priority concerns. The Foundation’s research agenda is developed through a process of consultation with Foundation subscribers and other drinking water professionals. The Foundation’s Board of Trustees and other professional volunteers help prioritize and select research projects for funding based upon current and future industry needs, applicability, and past work. The Foundation sponsors research projects through the Focus Area, Emerging Opportunities, and Tailored Collaboration programs, as well as various joint research efforts with organizations such as the U.S. Environmental Protection Agency and the U.S. Bureau of Reclamation.

This publication is a result of a research project fully funded or funded in part by Foundation subscribers. The Foundation’s subscription program provides a cost-effective and collaborative method for funding research in the public interest. The research investment that underpins this report will intrinsically increase in value as the findings are applied in communities throughout the world. Foundation research projects are managed closely from their inception to the final report by the staff and a large cadre of volunteers who willingly contribute their time and expertise. The Foundation provides planning, management, and technical oversight and awards contracts to other institutions such as water utilities, universities, and engineering firms to conduct the research.

A broad spectrum of water supply issues is addressed by the Foundation's research agenda, including resources, treatment and operations, distribution and storage, water quality and analysis, toxicology, economics, and management. The ultimate purpose of the coordinated effort is to assist water suppliers to provide a reliable supply of safe and affordable drinking water to consumers. The true benefits of the Foundation’s research are realized when the results are implemented at the utility level. The Foundation's staff and Board of Trustees are pleased to offer this publication as a contribution toward that end.

Roy L. Wolfe, Ph.D. Robert C. Renner, P.E. Chair, Board of Trustees Executive Director Water Research Foundation Water Research Foundation


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ACKNOWLEDGMENTS

The authors appreciate the support of the North Shore Water Commission and Water Research Foundation for this project.

Thanks are also extended to Frank Blaha, Water Research Foundation Project Manager and the Project Advisory Committee members for their excellent guidance during the project. Committee members are:

Andrew L. Reid, P.E. of AECOM, Concord, MA Anne Spiesman, P.E. of Washington Aqueduct, Washington, D.C.


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EXECUTIVE SUMMARY

OBJECTIVES This study demonstrates the use of three tools for process control in water systems. One

is a simple data management tool for making sense of complicated systems: Shewhart control charts used in industrial quality control. Another tool is a relatively simple means of tracking water quality at consumers’ taps: standardized monitoring stations that are abstractions of consumers’ plumbing systems. The third tool is a monitoring strategy that identifies key information linking components of a water system together.

The use of these tools is demonstrated in the context of a comprehensive consumer-centric process control method for drinking water systems.

BACKGROUND

There are multiple opportunities, yet unrealized in many drinking water systems, to

improve water quality and to save money in doing so. This is a surprising statement in that modern water systems already include the latest in technology to monitor various physical and chemical aspects of the water system. However, there are three ways to more effectively use the monitoring information for comprehensive process and quality control of a water system:

1. Be comprehensive: Identify specific information that links the water sources,

treatment, distribution system, and delivered water quality together. 2. Be consumer-centric: Use product quality – that is, delivered water quality – as

the main focus of process control endeavors, allowing it to drive decision-making, expenditures, and planning for the complete water system.

3. Enhance process control: Be proactive in water quality control and improvement rather than allowing regulatory compliance and customer complaints to motivate such efforts.

Three tools can be used to more effectively utilize monitoring information in this way.

For a comprehensive view of the water system, this project uses a monitoring strategy to identify key water quality parameters that link components of the water system together (Cantor 2009). To capture water samples representative of household plumbing connected to the water distribution system, this project uses a standardized distribution system monitoring station called a PRS Monitoring Station (Cantor et al. 2000, Cantor 2008, 2009, 2010, 2011). Any similar monitoring stations based on the Water Research Foundation pipe loop apparatus (AwwaRF and DVGW-TZW 1996) can be used. For meaningful data interpretation, this project uses techniques of industrial quality control and process improvement that were developed in the 1930’s (Wheeler and Chambers 1992).

These tools are used in an iterative process, where current information is obtained in order to improve the system in the future. After the improvement, more information is gathered to fuel further improvement. The iterative method is emphasized in this project as a comprehensive consumer-centric method of process control for drinking water systems.

Implementing this operational philosophy will achieve:


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A proactive approach to water quality with a lower possibility of falling out of compliance with drinking water regulations and a lower frequency of consumer complaints

A documented decision-making process that is transparent to consumers, produces consistent water quality, and gives managers and water commissioners confidence in the decisions made.

APPROACH

The water system of the North Shore Water Commission (NSWC) serving Glendale,

Whitefish Bay, and Fox Point in Wisconsin was used to demonstrate this method. The water system draws water from Lake Michigan and serves about 34,000 people. NSWC had previously used the delivered water quality monitoring technique to watch lead and copper concentrations in the water during a major system change from free chlorine to chloramine disinfection. The disinfection switch was successful, predictable, and controllable using this method. This subsequent demonstration project was carried out to show that on-going routine monitoring can serve as a means of comprehensive consumer-centric process control and process improvement. To carry out the previous and the current project, three essential tools were used.

First, water quality data studied in the project followed the monitoring strategy in a CRC Press book, Water Distribution System Monitoring: A Practical Approach for Evaluating Drinking Water Quality (Cantor 2009) and included parameters that:

describe the water “type” define the biostability of the water track water treatment chemical addition track source water and pipeline contaminants and debris track metals released from piping material to the water

Next, standardized monitoring stations were strategically placed in the distribution

system to routinely characterize water delivered to consumers. Any apparatus based on the Water Research Foundation pipe loops apparatus (AwwaRF and DVGW-TZW 1996) can be used for this purpose. In this project, PRS Monitoring Stations were used. The stations were re-engineered from the Water Research Foundation pipe loop apparatus concept in 1997 but greatly improved in 2006. The monitoring stations are described in detail in the CRC Press book mentioned above and are an open source technology.

The third tool is the use of Shewhart control charts for interpretation of monitoring data. These charts have been used in industrial quality control and process improvement since the early Twentieth Century. Shewhart control charts are described in detail in a SPC Press book, Understanding Statistical Process Control (Wheeler and Chambers 1992) with basic formulae and usage demonstrated in this report.

In addition, other data were gathered and compared to the monitoring stations’ water sample data. The data sources included:

Water quality data from four residences visited three times during the one-year

monitoring period


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Lead and Copper Rule compliance sampling data from residences throughout the water distribution systems since 1992

Chemical and microbiological data from the examination of monitoring stations’ internal metal plates after a one year exposure to the system water

Water treatment plant data collected at the SCADA system, including physical data and on-line sensor chemical data

Regulatory distribution system data Water quality data from the previous monitoring station project at NSWC Water quality data from previous monitoring station and pipe loop projects in

other water systems

RESULTS/CONCLUSIONS

This study demonstrated the basic structure of a comprehensive consumer-centric process control methodology. Trends in water quality at consumers’ taps were characterized. The information was compared to trends in the same parameters at the distribution system’s entry point. The information was also compared to physical and chemical aspects of the water treatment process and influent source water. In this way, effects on the consumers’ water quality could be observed as seasonal, unplanned, or intentional changes occurred anywhere in the water system.

In carrying out this consumer-centric method, obtaining water samples from the distribution system that are representative of the water quality that the consumer drinks is essential. The monitoring station data were shown to be indistinguishable from residential data for influent water quality. For lead, the monitoring station data were shown to be equivalent to residential water samples taken directly from lead service lines. In lead service lines, the lead-surface-area-to-water-volume ratio is similar to that in a monitoring station. But, this ratio is lower in a first-draw residential sample where plumbing materials other than lead exist. Therefore, the monitoring station lead concentration data are higher than first-draw residential sample data. The same similarity applies to the copper concentrations found in the monitoring stations. However, there are typically more copper components associated with a first-draw residential sample and, therefore, it is expected that monitoring station copper concentration data and first-draw residential sample copper concentration data will be closer in magnitude than the comparative lead data previously mentioned. This was shown to be the case. Another important comparison between residential data and monitoring station data is microbiological activity, which was also found to be similar. Therefore, the monitoring stations appear to be viable tools for capturing information about consumers’ water quality.

In addition to the water quality data taken from the monitoring stations, the internal metal plates of the PRS Monitoring Stations provide profound information on the chemical and microbiological interactions between the water and metal surfaces in the distribution system. At the end of a year of exposure to the system water, the metal plates were removed from the monitoring stations and the surface scales and films that had developed over time were studied chemically and microbiologically. This gives information similar to studying the scales and films on existing water system pipes. The information reveals the chemical compounds and biofilms that are able to form in the water environment and, therefore, defines the environment in the water system. In this way, the major factors and parameters that shape the water quality in a specific water system are identified.


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In this project, it was found that biofilms had formed on both the lead and copper plates. It was also found that aluminum from the alum added at the water treatment plant had precipitated significantly on the lead plates but not the copper plates. It is suspected that the crumbling or dissolution of aluminum scales with changing water environment conditions may control lead release into the water. A compound of lead and phosphate, which is assumed to slow lead release to water, was also present within the aluminum scales. Copper showed little signs of aluminum or phosphate scales and its release to water is possibly dependent on the oxidation of copper and on microbiological activity.

Readily available and meaningful interpretation of water system data is also essential for effective process control. The Shewhart control charts were shown to be as useful for water system and monitoring data as they have proved to be in industrial quality control and process improvement. The charts’ definition of typical variation of a parameter is useful for many operational functions, such as signaling atypical behavior in the water system, providing a gauge of process improvement, and documenting consistent, high quality water in the distribution system.

The comprehensive approach demonstrated in this project also illuminated the fact that a multitude of factors influence water quality. If only one factor is considered, incorrect conclusions might be made about a causal relationship between that factor and the resulting water quality. In this project, an intentional decrease in orthophosphate concentration corresponded with an increase in the lead concentration. However, data gathered on other water quality parameters in the distribution system and on water treatment operations raised doubts that the orthophosphate dosage change significantly affected the lead concentration. Hypotheses were formed concerning seasonal conditions leading to lead release and to the role that alum dosing in the water treatment process might play. If the project were to have continued past its planned end, these hypotheses would have been tested by continued strategic monitoring, changes made to operations based on the findings, monitoring continued to confirm results, and so on in an iterative process of system improvement.

In summary, routine gathering of the water system information, graphing of the information on Shewhart control charts, and discussion of the charts in operational, management, and planning meetings provide in-depth feedback on the operation of the water system. Taking action on behalf of system improvement and stability is the next step. Then, the cycle repeats. This is comprehensive consumer-centric process control.

APPLICATIONS/RECOMMENDATIONS

The report suggests a step-by-step approach in adopting this method of comprehensive

consumer-centric process control.

1. Use readily available water system data with Shewhart control charts to begin studying data.

a. For data on the consumers’ water quality, use existing regulatory distribution system disinfection data collected around the distribution system for The Total Coliform Rule.

b. Use existing SCADA and on-line sensor data for other water system data.


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c. For each water quality parameter at each sampling site, enter the dates and data values into a data entry spreadsheet of the Excel® add-in that comes with this report. Click the button to create the Shewhart control chart.

2. Evaluate the Shewhart control charts created in Step 1. a. When have any of the parameters exhibited a significant change from

typical water system operation as defined by the Shewhart control chart rules of interpretation discussed in this report?

b. Do the data exhibit any trends? c. Do parameters have a wide or narrow variation? d. Do average values of the parameters meet water system goals? e. What operational changes might bring parameters to the desired average

values and narrow the variability of the parameters? 3. Make operational changes proposed in Step 2 and continue to collect and study

the readily available water system data. a. Are the new goals being met? b. Adjust operations accordingly. c. Iterate steps 1, 2, and 3 for process improvement.

4. When comfortable with this method, carry out a more comprehensive monitoring strategy to build a bigger picture of delivered water quality.

a. Add the study of metal transfer and biostability to the monitoring strategy by using one or more monitoring apparatuses (derived from the pipe loop concept) strategically placed in the distribution system.

b. Join industry-wide water system improvement efforts, such as those promoted by the American Water Works Association, for encouragement and general guidance.

5. Keep the monitoring efforts going. A comprehensive consumer-centric process control methodology includes an iterative series of monitoring, evaluation, and decision-making. That is, collect data, create charts, apply the chart rules of interpretation, discuss charts in daily, weekly, and planning meetings, troubleshoot issues, make system changes for improvement, collect data, … etc.

BENEFITS TO WATER UTILITIES

There are many benefits of adopting this method:

System operation o Triggering the need to troubleshoot equipment or other system operations o Saving money on treatment chemicals o Minimizing temporary water quality degradation o Evaluating system operations routinely

Lead and Copper Rule Issues o Studying lead and copper transfer to water in the context of the whole

water system with its multitude of influencing factors o Serving as a surrogate for residential sampling so that lead and copper

concentration trends can be known routinely instead of being glimpsed at every three years


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o Aiding in the determination of the mechanism or mechanisms of lead and copper transfer into the water specific to the water system

o Establishing key water system water quality parameters that are relevant to the individual water system instead of general parameters listed in a regulation to be applied to all water systems

o Monitoring and controlling the key water system water quality parameters, especially those that can be controlled at the water treatment plant to keep them at the desired level with narrow variation; variation of key parameters can cause variation in lead and copper release to water

o Monitoring and controlling system transitions, such as changes in corrosion control chemicals, changes in water sources, and changes in disinfection or other water treatment, in order to achieve simultaneous compliance with drinking water regulations

o Determining the need for corrosion control chemicals o Comparing and selecting corrosion control chemicals

Beyond Lead and Copper Rule Issues o Assessing and controlling biostability of water, aiding in setting an

appropriate disinfection concentration, and staying in compliance with the Total Coliform Rule

o Assessing cleanliness of the piping system o Developing hypotheses for further research on a system level and on a

national level A Comprehensive Consumer-centric Process Control Methodology

o Establishing a water quality control methodology o Establishing a water system process improvement methodology

MULTIMEDIA

A Microsoft Excel® add-in is provided with this report so that the reader can begin to

work with Shewhart control charts on data taken over time. The add-in is included with the report on CD-ROM. The add-in calculates Shewhart control chart statistics and creates graphs of time-series data with the associated statistics. RESEARCH PARTNER

North Shore Water Commission

PARTICIPANTS

Principal Investigator and chemical engineer: Abigail F. Cantor, P.E.; Process Research Solutions, LLC; Madison, WI Water system management and operation, water sampling, on-site water analyses: Eric Kiefer, Manager; North Shore Water Commission; Glendale, WI


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Statistician: Kevin Little, Ph.D.; Informing Ecological Design, LLC; Madison, WI Microbiologist and microbiological analysis of metal plate biofilms: Andrew Jacque, P.E., Ph.D.; University of Wisconsin-Platteville; Platteville, WI Microbiologist and microbiological analysis of metal plate biofilms: Archie Degnan, Senior Microbiologist; Wisconsin State Laboratory of Hygiene; Madison, WI Geologist, geochemist, metallurgical analysis of metal plate scales: Barry Maynard, Ph.D.; University of Cincinnati; Cincinnati, OH Physicist and metallurgical analysis of metal plate scales: David Mast, Ph.D.; University of Cincinnati; Cincinnati, OH Project planning and industrial quality control: Judith Cantor; Acer Enterprises, Inc.; Raleigh, NC


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CHAPTER 1 INTRODUCTION

There are multiple opportunities, yet unrealized in many drinking water systems, to

improve water quality and to save money in doing so. This is a surprising statement in that modern water systems already include the latest in technology to monitor various physical and chemical aspects of the water system. However, there are three ways to more effectively use the monitoring information for comprehensive process and quality control of a water system:

1. Be comprehensive: Identify specific information that links the water sources,

treatment, distribution system, and delivered water quality together. 2. Be consumer-centric: Use product quality – that is, delivered water quality – as

the main focus of process control endeavors, allowing it to drive decision-making, expenditures, and planning for the complete water system.

3. Enhance process control: Be proactive in water quality control and improvement rather than allowing regulatory compliance and customer complaints to motivate such efforts.

BASIC WATER SYSTEM PROCESS CONTROL

The essential elements of process control in modern water systems are the SCADA system, the operator, and the manager.

System-wide Supervisory Control and Data Acquisition (SCADA) technology identifies the physical state of the water system – filter tanks in service or being backwashed, pumps running or on stand-by, valves open or closed, flow rates, pounds of chemicals fed, etc. It also gathers information from on-line chemical sensors – disinfection concentration, turbidity, chemical security data, etc. Only through a SCADA system can the numerous details of system operation be watched.

Even with this electronic information, an operator – a flesh and blood human being – is necessary to perform an inspection cycle. The SCADA system may register a flow rate, but an operator may find a leak in the same pipe where flow is being measured and find the polymer for water treatment, for example, all over the floor. In addition, the operator must maintain and calibrate the chemical sensors. And, the operator must gather data, such as by running some chemical analyses in the field, to fill in gaps where on-line sensors are not available or are not possible. The operator then must make operational decisions and take action.

The manager plays a role, not only in daily decision-making, but also in looking for monthly and yearly trends in this detailed daily operational information and tweaks the daily operation to improve these trends. The manager also facilitates the planning for future water system operations, sometimes by bringing in water system design experts to extrapolate to future system needs.


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WATER QUALITY EFFORTS MOTIVATED BY REGULATORY COMPLIANCE AND CONSUMER COMPLAINTS

Of course, even with all the detailed operational activity consuming the operators’ and managers’ time, the overall concern is for providing safe and pleasing drinking water to consumers. The drinking water profession proudly acknowledges its direct responsibility of “protecting public health through continual improvement” (Hoffbuhr 2006). However, it has been historically difficult to routinely measure the actual characteristics of the consumers’ water. Variation in distribution system location and the individual characteristics of the consumer’s premise plumbing along with the sheer number of consumer taps make this a difficult, if not impossible, task. This difficulty is realized in carrying out regulatory Lead and Copper Rule sampling where water utility employees must obtain water samples from consumers after a six hour minimum stagnation of water in the premise plumbing (CFR 40:141:I). Consumers soon tire of providing samples to the water utility, making the technique not practical for routine monitoring, and that is for only two water quality parameters – total lead and copper concentrations. By default, this difficulty in obtaining water samples representing the consumers’ water quality has lead to two main motivational forces to act directly on behalf of consumer water quality.

One motivating force is consumer complaints. Water utility personnel look at trends in complaints to determine if a system-wide issue is occurring. Then, they must take action accordingly, devoting time to remedying the specific issue and reacting quickly so as to maintain the consumer’s confidence in the quality of the drinking water. It would be preferable to detect and head off more of these issues before they affect the consumer.

A second motivating force to directly improve the consumer’s water quality is the dictates of the Federal and State drinking water regulations. Utility personnel monitor the water system at particular times according to these dictates and must respond quickly if the system is out of compliance at the time of data gathering. It would be preferable to have knowledge of the compliance status routinely, if it were possible to do so within a reasonable budget.

In addition, these two methods of addressing water quality do not address the delicate balance that must be achieved to satisfy competing chemical requirements. That is, complying with one regulation may send a water system out of compliance with a different drinking water regulation; attending to one consumer complaint may create other problems. Assessment of monitoring data needs to acknowledge the intricate inter-relationships and inter-dependencies of water system characteristics.

In addition, both of these motivational means to act on behalf of water quality encourage reaction after a water quality problem has occurred. This can sometimes be more expensive and dire than preventing a water quality issue from occurring in the first place. CURRENT FOCUS OF WATER SYSTEM PROCESS CONTROL

In summary, modern drinking water systems focus on a multitude of daily details of operation, responding to meeting various criteria so that water flows through the system and is delivered to the consumer. In many cases, adequate water quality reaching the consumer is assumed. Water quality actually received by the consumer is many times assessed in the light of the utility’s compliance with regulatory drinking water standards and when called into question by a consumer complaint.


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This is not to say that modern water utilities do not attempt to step further into process and quality control. Special investigations of water quality are performed by utilities – especially larger utilities that have more resources than smaller ones. Jar tests are used to study the outcomes of various chemical scenarios and apparatuses of both new and harvested water pipe (pipe loops) are set up to, off-line, test effects of chemical scenarios on lead, copper, and iron concentrations in drinking water. However, the ability to routinely know the status of water quality as experienced by consumers is needed.

The drinking water industry has been realizing this need over a number of years. Originally, ensuring that high quality water entered the distribution system was sufficient. However, over the years, the drinking water industry’s focus has changed to improving the operation of the distribution system because of the discovery of 1) carcinogenic disinfection by-products that can form in the distribution system, 2) excessive lead and copper concentrations that can be transferred from plumbing materials into the drinking water, 3) pathogenic microorganisms that can infiltrate and grow in the distribution system, and, 4) the negative roles that accumulation of chemical scales and biofilms can play on water quality.

With this focus on changes that can occur in water during its time in the distribution system, it becomes evident that lack of routine information about delivered water quality results in inadequate operational decisions and a lack of feedback on the effects of implemented changes. While current efforts to operate the water system are well-intentioned, this is water quality by guesswork which can easily become a crisis.

As an example, take the case of a lead and copper corrosion control treatment being implemented. A phosphate-based corrosion control chemical may be selected based on anecdotal evidence that it held lead and copper concentrations down in another water system. The chemical is pumped directly into the distribution system. There is no feedback as to any effects or side-effects of the chemical on a routine basis. Instead, months will go by until Lead and Copper Rule sampling is performed to check if lead and copper concentrations are indeed being suppressed at the consumers’ faucets. Even then, it can be argued that Lead and Copper Rule sampling does not adequately assess the effects of the corrosion control chemical. Such was the case when one water utility found elevated copper in new plumbing systems after the introduction of a corrosion control chemical (Cantor et al. 2000); new copper plumbing systems are not included in Lead and Copper Rule sampling so the problem missed detection at first. It took over a year for the problem to come to light in the form of a consumer complaint of health problems from copper poisoning. This is not an appropriate approach to process control in terms of timely feedback of effects or in terms of risks to consumers.

In another example, a water system needed to expand the distribution system to meet the needs of a growing city. The expansion greatly increased the time that water stayed in the system. No provisions were made to boost disinfection during that extended time. This situation left new buildings built in the new distribution system section with greatly elevated copper levels in the drinking water from microbiologically influenced corrosion (PRS 2009).

These are examples of making system changes without direct knowledge of the effects of the system changes on the consumer.


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CURRENT EFFORTS TO CHANGE WATER SYSTEM PROCESS CONTROL PERSPECTIVES

To prevent the occurrence of such crises, a comprehensive approach to water system management is being promoted on three main fronts. The American Water Works Association (AWWA) has developed a series of management standards (G100 Water Treatment Plant Operations and Management, G200 Distribution systems Operations, G300 Source Water Protection, G400 Utility Management System) and management guides. AWWA also offers the Utility Quality Program, an evaluation and recognition program for water utilities that follow the management standards (AWWA 2011).

On another front, a group of organizations (which includes AWWA) put together a committee to promote effective water and wastewater utility management. The committee held forums to define the “building blocks” of management and documented their findings in a “primer” on effective utility management in 2008 (APWA et al. 2008).

There is also a Partnership for Safe Water which is made up of six water organizations (which includes AWWA) and more than 200 water utilities. The organization formed after a 1994 report by the U.S. Environmental Protection Agency described a number of drinking water standard violations around the country. The group includes a Water Treatment Program and a Distribution System Optimization Program (PSW 2011). Over the years, the group has developed guidelines for water system optimization. A 2010 Water Research Foundation project helped the group develop criteria for optimized distribution systems (Friedman et al. 2010). This group’s focus on the distribution system is further acknowledgement of increasing concern about changes to water quality as the water travels to the consumer.

A FOCUS ON PRODUCT QUALITY IN A WATER SYSTEM

In the primer on Effective Utility Management, “Product Quality” is one of ten attributes of effectively managed utilities. The other attributes are: Customer Satisfaction, Employee and Leadership Development, Operational Optimization, Financial Viability, Operational Resiliency, Community Sustainability, Infrastructure Stability, Stakeholder Understanding and Support, and Water Resource Adequacy. The ten attributes are presented visually in a circle with the report stating that there is no particular order to the attributes, just opportunities for improving management and operations (APWA et al. 2008).

On the other hand, The Partnership for Safe Water states: “Optimization must evaluate the best options to produce the highest quality water first and then consider the effect on the plant as a whole…The objective is to seek optimum performance to produce highest water quality at all times.” (Lauer 1997)

This report’s philosophy also emphasizes water quality before other operational aspects and adheres to putting the consumers’ experience of water quality at the forefront. In this project, it is encouraged that all operational activities and planning at a water utility elicit the thought: How does that directly affect the water quality at the consumers’ taps?


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A COMPREHENSIVE, CONSUMER-CENTRIC PROCESS CONTROL METHODOLOGY FOR DRINKING WATER SYSTEMS

Until recently, tools have not been available to routinely characterize the water quality at the consumers’ taps without visiting residences. This project uses a standardized distribution system monitoring station, first developed in 1997 and greatly improved in 2006, to capture water samples representative of household plumbing connected to the water distribution system (Cantor et al. 2000, Cantor 2008, 2009, 2010, 2011). For a comprehensive view of the water system, this project uses a monitoring strategy to identify key water quality parameters that link components of the water system together (Cantor 2009). For meaningful data interpretation, this project uses techniques of industrial quality control and process improvement that were developed in the 1930’s (Wheeler and Chambers 1992). These tools are discussed further in the Methods and Materials section of this report.

These tools are used in an iterative process, where current information is obtained in order to improve the system in the future. After the improvement, more information is gathered to fuel further improvement. The iterative method is emphasized in this project and translated in Figure 1.1 as a comprehensive consumer-centric method of process control for drinking water systems.

Implementing this operational philosophy will achieve:



This project demonstrating the use and benefits of these tools came from intensive work

with the tools in three water systems starting in 2006. In one of the water systems, North Shore Water Commission (NSWC), located in Glendale, Wisconsin, the techniques guided the water system through a major system change from free chlorine to chloramine disinfection along with a change in corrosion control chemical. Using the standardized monitoring stations in the distribution system, lead and copper data representative of the consumers’ water quality were routinely obtained. Analyzing the data with statistical techniques used in industrial quality control, it was shown that the changes lowered the transfer of the metals into the water, lowering the lead and copper average concentrations, and in equal importance, tightening up the range of concentrations through which the lead and copper were seen to vary. This successful and beneficial application of the technique motivated the management at NSWC to continue monitoring for further process improvement. Working with Water Research Foundation, the funding for continued monitoring has been shared and the experience presented in this report is described for the benefit of other water systems. OBJECTIVES OF THIS STUDY

This study demonstrates the use of three tools for process control in water systems. One is a simple data management tool for making sense of complicated systems: Shewhart control


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charts used in industrial quality control. Another tool is a relatively simple means of tracking water quality at consumers’ taps: standardized monitoring stations that are abstractions of consumers’ plumbing systems. The third tool is a monitoring strategy that identifies key information linking components of a water system together.

The use of these tools is demonstrated in the context of a comprehensive consumer-centric process control method for drinking water systems.


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Figure 1.1 A comprehensive consumer-centric process control method for drinking water systems


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CHAPTER 2 METHODS AND MATERIALS

WATER SYSTEM

This project was initiated by North Shore Water Commission (NSWC) of Glendale, Wisconsin. NSWC is composed of three suburban Milwaukee water distribution systems – Whitefish Bay, Glendale, and Fox Point – that have combined their resources to produce treated drinking water drawn from Lake Michigan. The population served is around 34,000 people.

As displayed in Figure 2.1, water treatment includes:

Addition of potassium permanganate to control zebra and quagga mussels, as needed during warmer months

Addition of alum and polymer for coagulation Addition of activated carbon for adsorption of compounds causing taste and odor,

as needed Flocculation and sedimentation Rapid sand filtration using anthracite carbon, sand, and gravel UV disinfection Addition of fluoride Addition of 10/90 polyphosphate/orthophosphate blend for corrosion control (The

desire is to use an orthophosphate corrosion product. This blend with very low polyphosphate is the only product available from the supplier that is essentially orthophosphate.)

Addition of sodium hypochlorite Storage in a reservoir Addition of ammonium hydroxide to convert free chlorine to chloramine residual

disinfection TOOL #1: STRATEGY OF MONITORING Representative Water Quality Parameters

The project’s monitoring plan was developed using a strategy of linking together all components of the water system – from the water source through water treatment, the distribution system, and finally, the water quality delivered to the consumers. In the beginning, the factors that most significantly shape and define the delivered water quality are not known. A number of factors and their associated water quality parameters are monitored to determine which appear to be the most influential on resulting water quality. The parameters are selected by considering some common factors that can affect water quality as described elsewhere (Cantor 2009) and as summarized into the following categories:

Parameters that describe the water “type” Parameters that define the biostability of the water Parameters that track water treatment chemical addition Parameters that track source water and pipeline contaminants and debris


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Courtesy of North Shore Water Commission Figure 2.1 Schematic of water treatment at North Shore Water Commission


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Parameters that track metals released from piping material to the water Water quality parameters are discussed here within the context of their categories. Some

nuances of sampling for or analyzing of the parameters will be noted. However, refer to other sources for definitions and details of analysis (APHA et al. 2005).

Water Type

The “type” of water in a water system is generally described by alkalinity, hardness, total dissolved solids, pH, and temperature. These parameters can also be used in a calculation as an estimate of dissolved inorganic carbon (DIC) concentration, where DIC and pH are used as a gauge of uniform corrosion of metals (AwwaRF and DVGW-TZW 1996).

Biostability

Biostability of water refers to the balance of nutrients in water that encourage the growth of microorganisms with disinfection available in the water to stop their growth (Van der Kooij 1992, Volk and LeChevallier 2000). Water quality parameters of nutrients are those that measure nitrogen compounds (ammonia nitrogen, nitrite nitrogen, and nitrate nitrogen), phosphorus compounds (total phosphorus), and carbon compounds (assimilable organic carbon, aka AOC).

Nitrogen compounds are of special interest in terms of a microbiological phenomenon called “nitrification”, which can cause corrosion of metals. Tracking and preventing nitrification is important in water systems that use chloramine disinfection, where chlorine and ammonia are combined to form monochloramine as the active disinfectant. The chloramine compounds can degrade in the distribution system, releasing free ammonia to the water. The ammonia can, in turn, be used by nitrifying bacteria to produce nitrite; other nitrifying bacteria can use the nitrite to produce nitrate. In this project, free ammonia was measured in the water flowing into the monitoring stations and residences. After a period of water stagnation, if nitrification is occurring, lower free ammonia concentration, higher nitrite concentration, and sometimes higher nitrate concentration is expected.

Phosphorus concentration is insignificant in the source water for NSWC. However, a phosphorus product is added in the hopes of controlling lead and copper release in the distribution system. Ninety to one hundred percent of the total phosphorus in the water comes from the orthophosphate fraction of the phosphorus product. Up to ten percent of the total phosphorus in the water comes from the polyphosphate fraction of the phosphorus product where the polyphosphate eventually breaks down into orthophosphate at various locations in the distribution system. However, the total phosphorus analysis is an expensive analysis which includes heat treating (digestion) of the water sample in the laboratory; the orthophosphate analysis can be performed in a simpler manner and can be performed at the water utility’s laboratory. In this project, only orthophosphate concentration was used as a measure of phosphorus and was determined in NSWC’s laboratory. Being able to measure this parameter more frequently not only gives a measurement of that nutrient but also provides feedback on dosage of orthophosphate for the purpose of corrosion control.

AOC is a measure of carbon compounds in the water that are readily accessible for microorganisms to use as a nutrient. These are smaller carbon compounds that the


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microorganisms can utilize. It is analogous to a person being offered a hamburger for lunch or a whole cow. The person will most likely take the quicker route to lunch and order the hamburger (Cantor 2009). AOC is discussed in a number of articles on biostability (Volk and LeChevallier 2000, Escobar and Randall 2001, Zhang et al. 2002). An often quoted article states that, without disinfection, AOC must be less than 10 µg/L in order to prevent microbiological growth (Van der Kooij 1992). At a concentration of 100 µg/L, strong disinfection levels must be maintained to counteract growth potential (Volk and LeChevallier 2000, Zhang et al. 2002). AOC is a difficult and very expensive analysis involving microbiological cultures; few laboratories can perform this analysis. AOC is a subset of Total Organic Carbon (TOC), a more common analysis, but a TOC analysis does not reveal the information on AOC that is needed. The AOC analysis has a precision of +/- 17.5% (APHA et al. 2005). As the results get closer to the reporting limit (10 µg acetate-carbon/L or ACE/L), the more susceptible the method is to contamination. There will sometimes be growth in the analytical blanks which is reported if the blank result exceeds 30 µg ACE/L.

Disinfection to counteract the nutrients in the water is measured with total chlorine or monochloramine analyses. For water systems using chloramine disinfection, monochloramine is the active disinfection fraction of the total chlorine in the water. The fraction of total chlorine that is monochloramine is affected by various environmental conditions such as the original ratio of chlorine to ammonia used to produce the chloramine compounds and the pH of the water.

Finally, when biostability is tracked, it is good to have a measure of microbiological activity. Microbiological activity is difficult to measure because of the life cycles of the microbiological communities. There are also many types of microorganisms that can be involved, each group with its own characteristics. A common and inexpensive indicator of microbiological activity is heterotrophic plate count (HPC), which quantifies colony-forming units of heterotrophic bacteria. By using it as an indicator, one assumes that if heterotrophs are present, other types of microorganisms will also be present to the same degree. The HPC analyses performed in this study were made more sensitive by using R2A growth media in the Petri dish for incubation; this analysis is gentler and it allows more microorganisms to survive to be counted than other HPC methods. The HPC_R2A test should be used as an indicator of microbiological activity with caution. There are situations where few heterotrophs are present but there are other thriving groups of microorganisms.

An alternative method for quantifying microbiological activity called microbiological growth indicator (MGI) was experimented with in this project. The method is typically used on wastewater and this was an attempt to adapt the method to drinking water. The MGI assay utilizes a tetrazolium salt together with a chemical that acts as an electron shuttle to detect total electron activity, presumably associated with microbial respiration (Chaieb et al. 2011, McCluskey et al. 2005, Tsukatani et al. 2010). Electron activity can then be measured spectophotometrically by quantifying the electron reduction of the salt to a formazan and/or the electron shuttle to a different chemical form. See Appendix A for the details of the test. The objective of the MGI assay was to determine whether a correlation exists between heterotrophic plate count data (HPC) and total electron activity within a water sample using the MGI assay. The tests did show higher electron activity in proportion to the HPC results when iron bacteria were present. This was a desired result in that HPC results exclude certain types of iron bacteria that are solely autotrophic and not heterotrophic. However, the MGI method was found to have interference by the presence of dissolved metals. To account for this interference, background analysis of electron activity was included by splitting the sample and analyzing the filtrate.


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Comparison of the two assays, by taking the absorbance values associated with the MGI assay minus the absorbance values associated with the filtered MGI assay, gives an assessment of total electron activity associated with microbial occurrence. A problem with MGI that could not be overcome was that a true association of the number of microbes present per unit change in absorbance was difficult to establish due to the diverse population that could be present and the associated physiology.

The MGI test would need more development to be a readily available technique for quantifying microbiological activity. An alternative is a test based on the measurement of ATP (Adenosine Triphosphate), the primary energy transfer molecule for living cells (Luminultra 2011). Measurement of ATP is said to be a direct indication of the level of total microbial contamination in water. Yet another test that is developing is quantitative real time polymerase chain reaction (QPCR), which detects and quantifies one or more specific sequences in a DNA sample. These alternative analyses were not available for this project.

Measurement of biostability is a developing concept and there are not yet standardized tests to define it. HPC using R2A growth media is an analysis available in many laboratories for a reasonable fee and will be used in projects such as this until a more precise measurement can become as prevalent and economical. Chemical Addition

Several chemicals are added for water treatment at NSWC. In this study, components of those chemicals were tracked for various reasons. Disinfection chemicals and phosphate corrosion control product chemicals have already been discussed. In addition, alum (aluminum sulfate) is of interest. Aluminum has been identified as a metal that can precipitate in the distribution system and form a scale on the pipe walls. The aluminum compounds can sorb other metals (such as lead and copper) that flow by, store those metals on the pipe scale surface, and randomly release those metals into the water in higher concentrations than they would normally be found.

In the NSWC system, chloride is found naturally in the source water; sulfate is mostly from chemical addition of alum. The presence of chloride in water in relation to sulfate has been hypothesized as a mechanism of increased galvanic corrosion. Galvanic corrosion occurs when dissimilar metals are placed in contact with each other. Depending on the metals involved, electrons flow from the metal that has a tendency to give up electrons to the metal that has a tendency to accept electrons. The metal that gives up electrons goes into solution with adjacent water and is said to “corrode”. One group of researchers has found that galvanic corrosion at connections of dissimilar metals, such as at a typical partial lead water service line replacement, is significant and persistent when the chloride to sulfate mass ratio is greater than 0.5 (Triantafyllidou and Edwards 2006, 2010). Another group of researchers has found that galvanic corrosion at locations like partial lead service line replacements is short-lived and highly localized, rendering it not a significant mechanism of metal release into water (Boyd et al. 2010). This project measures and graphs the chloride to sulfate mass ratio to determine if any changes occur over time but does not address galvanic corrosion.


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Contaminants and Debris

Besides chemicals added to water for treatment purposes, chemical contaminants can enter with the source water and they can enter treated water from scales and films in the distribution system pipelines.

In addition, as previously mentioned, aluminum can form pipeline scales that sorb, store, and release lead, copper, and other metals. Manganese and iron scales can behave similarly (Maynard and Mast 2006).

In many water systems, a significant portion of iron in the water comes from scales of corroded iron components. Iron will typically be found in particulate form because the scales of iron corrosion products can crumble into the water. However, the iron scales, as well as scales of other chemical composition, can also dissolve into the water depending on changes in water chemistry, thereby increasing the dissolved metal concentration.

These metals can be measured in water individually. A measure of particulate chemicals in the water is turbidity. A measure of dissolved minerals in water is total dissolved solids (TDS).

Released Metals

Metals, such as lead, copper, and iron, can be released from piping materials in the distribution system. Both particulate and dissolved metal fractions are of interest. If a metal is mostly in particulate form, this may (but not necessarily) indicate release of pipeline scales and films or the presence of one type of microbiologically influenced corrosion. If a metal is in dissolved form, this may (but not necessarily) indicate a chemical dissolution of pipeline scales and films or the uniform corrosion of metal piping materials.

As stated previously, total and dissolved metal concentrations can be determined analytically. The difference between the two concentrations is particulate metal concentration. To measure total metal concentration, the sample is acidified so that any particulate metal is dissolved, joining the dissolved metals already present. To measure dissolved metal concentration, a portion of the sample is first filtered through a 0.45 micron filter to remove particulate metal larger than that size. Then, the sample is acidified to ensure that remaining metal is in dissolved form. It is very important not to acidify the sample until the particulate fraction has been filtered out; otherwise, the particulate metal will become dissolved and the information will be lost.

A dilemma exists in the filtering method. Ideally, the filtering should be performed in the field immediately after the sample has been taken. This is because the original fractions of particulate and dissolved metal can change rapidly and be lost. Particulate metal can re-dissolve in the sample bottle; dissolved metal can precipitate as particulates. Particulate metal can also adhere to the sample bottle wall. (Cantor 2006)

This project used a 50 mL sterile syringe and 0.45 micron syringe filter to process samples for dissolved metals in the field. This is still not an ideal situation. The reality of sampling is that the sample still cannot be processed immediately and the filtration method is awkward to use. Sometimes, in this project, the dissolved metal concentration was higher than the total metal concentration, which is not physically possible and indicates difficulty with the filtering technique.


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It may be better to collect the water sample, leave no air space in the bottle, place the bottle on ice for immediate shipment to the laboratory, and request immediate laboratory filtration upon arrival. The laboratory uses a cleaned filtration apparatus under more controlled conditions than a field filtration. The dissolved fraction in all the samples will all be relative to the lag time between obtaining the sample and filtering. Field and Utility Laboratory Analyses

Some water quality parameters must be measured as soon as possible after obtaining a sample or else the values will change by interacting with air or the sample bottle. In this project, field analyses were performed for such parameters: pH, temperature, total chlorine, turbidity.

The NSWC laboratory also has the capabilities to perform tests that must be run frequently for water system operation. NSWC laboratory analyses for this project included: HPC using the R2A growth media, monochloramine, free ammonia, nitrite, nitrate, and orthophosphate.

With the performance of water analyses comes the responsibility to show the precision of the method and any bias that may have existed during the measurement (APHA et al. 2005). This is a standard of practice in water analytical laboratories but is not typically used on field data. This project recognizes the need to use these quality control methods on field analysis data.

The quality control techniques described in Standard Methods (APHA et al. 2005) for laboratories use Shewhart control charts, which will be described in more detail later in this report. However, the Standard Methods technique uses the standard deviation of accuracy measurements as determined by the manufacturer of the standard solutions. In this project instead of the manufacturers’ standard deviations, the variability of the project’s routine measurement of standards was calculated using the “moving range” of the dataset as will be described later (Wheeler and Chambers 1992). The result is that the measurement of standards represents bias of this project’s analytical methods and not the manufacturers’ accuracy of producing standard solutions. Figure 2.2 is a summary of the measurement of analytical standards to determine bias. The measurement of each standard is compared to the standard’s nominal value; the measurement’s percent of the nominal value is calculated and is called “percent recovery”. The calculation might be higher or lower than 100 percent. The percent recovery is plotted on a Shewhart Individuals Chart to determine if there is any bias to the measurement. That is, do the measurements tend to be higher or lower than the nominal value? The control charts show how the measurements fluctuate around an average recovery percentage.

Table 2.1 summarizes the bias charts for the analyses. Both the average percent recovery and the width of the control limits are important. Table 2.1 shows that the turbidity, orthophosphate, and monochloramine analyses were less biased than the ammonia and nitrate analyses.

Figure 2.3 is a summary of analytical precision charts. To determine precision, selected samples were analyzed twice. The difference between results is calculated and graphed on a type of Shewhart control chart called a Range Chart with Subgroup Size Two (Wheeler and Chambers 1992). A range chart is used because precision of an analytical technique is defined by the variability of the difference between duplicate measurements. Calculations for a subgroup of size two are used because two measurements are made on the same sample for each check on precision. However, Standard Methods again calls for the use of a known standard deviation. The reference book does not explain how to calculate the precision when the standard deviation


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of precision is not known and only the duplicate data points are available for the calculation of control limits. In this project, the calculation of moving range was used as a basis for calculating the upper control limit of the range chart for the duplicate measurements. The upper control limit is used as the precision of the field test.

Figure 2.2 NSWC analytical measurement standards


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Table 2.1 NSWC analytical bias summary

Analyte Average Percent Recovery from Figure 2.2

Width of Control Limits (percentage points)

Turbidity 102 5 Orthophosphate 99 9 Monochloramine 101 10

Ammonia 110 125 Nitrate 98 70

(continued)

Figure 2.3 NSWC analytical measurement duplicates


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(continued)


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(continued)


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As with the bias measurements in this project, there were some infractions of control where data points exceed the control line and where data points linger on one side of the average. It should be noted that most nitrogen compound (ammonia, nitrite, and nitrate) results were at very low concentrations which are difficult to measure with these analytical methods, precision issues at these low concentrations are to be expected. Table 2.2 summarizes the estimated precision of each analysis.

Table 2.2 NSWC analytical precision based on Shewhart control chart estimates of

variation

Analyte Estimated Range of Analytical Precision to two significant digits from Figure 2.3

Turbidity 0 to 0.11 Orthophosphate 0 to 0.04 Total Chlorine 0 to 0.16

Monochloramine 0 to 0.15 Ammonia 0 to 0.01

Nitrite 0 to 0.01 Nitrate 0 to 0.17

From Figure 2.3, it is seen that precision for monochloramine was out of control in

summer 2010. This came about because of an issue of reaction time in the analysis of the samples. Different operators were using different reaction times. For precision in turbidity, there were also issues of consistency in measurements during summer 2010. There was also an issue in the age of the turbidity standards used.

Laboratory Analyses

Other water quality parameters were analyzed at a commercial drinking water certified laboratory (Underwriters Laboratories Inc. of South Bend, Indiana). Quality control of the analytical methods is handled within the laboratory. TOOL #2: CAPTURE OF WATER QUALITY EXPERIENCED BY THE CONSUMER Representative Water Distribution System Samples

One goal of this project was to demonstrate the capture of water quality data similar to the water quality that consumers in the NSWC water system experience. This was accomplished by the use of PRS Monitoring Stations, standardized monitoring stations for water distribution system monitoring, and will be shown to have been successful in the ‘Results’ section of this report.

The monitoring station is not proprietary; to the contrary, information about the concepts behind the station and instructions for assembly have been published as an open source technology (Cantor 2009). The PRS Monitoring Station has evolved from the general configuration of the Water Research Foundation’s pipe loop apparatus developed in the 1990’s,


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with information published in the same open source spirit as the pipe loops (AwwaRF and DVGW-TZW 1996).

One does not have to use a PRS Monitoring Station, per se, to obtain some information on water quality similar to the consumers’. The key is that water samples be taken in critical water distribution system locations. One critical location, the entry point to the distribution system, represents the situation where water and chemicals added to the water are the freshest and there has been little reaction time. Locations of high water age in the distribution system represent the opposite situation where the longest chemical and microbiological reaction time, as well as exposure to piping materials and pipe wall scales and films, has occurred.

At a minimum, regulatory distribution system disinfection data can be used as an indicator of consumer water quality. (This will be shown later in the report.) To gather more information and shed more light on consumer water quality, a number of water quality parameters from flowing water can be measured routinely at critical distribution system locations.

Most importantly, a further step can be taken to routinely watch the interaction of the water with metal surfaces. This information is profoundly revealing in the mechanisms that shape the consumers’ water quality. It makes sense that this is so as the consumer receives water after its long contact with piping materials and the chemical scales and biofilms that have built up on the piping surfaces. This type of information can be obtained from a PRS Monitoring Station or any apparatus similar to a Water Research Foundation pipe loop. The PRS Monitoring Stations are used in this project because they have overcome some configuration issues found in Water Research Foundation pipe loops. Also, Water Research Foundation pipe loops have traditionally been used to run isolated off-line tests. The PRS Monitoring Stations have been used to interact with water at various points in the distribution system and provide actual distribution system monitoring data.

Such was the motivation when NSWC management adopted the use of the PRS Monitoring Stations to monitor the consumers’ water quality during a major water chemistry change in the water system. NSWC was using a 50/50 polyphosphate/orthophosphate product for corrosion control and free chlorine disinfection. A change to chloramine disinfection was planned to take place at the end of 2008 to more easily stay in compliance with disinfection by-product regulations. NSWC management also wanted to proceed with this change with caution so that the water system did not fall out of compliance with lead and copper regulations, as other water systems have (Hill et al. 2011). Using the PRS Monitoring Stations, NSWC management was able to measure, view, and react to consumer water quality parameter trends, including lead and copper concentrations, before, during, and after the major chemical change.

In that project, NSWC used two PRS Monitoring Stations to routinely monitor at the entry point of the distribution system and at a location of high water age in the distribution system. One of the monitoring stations was also used briefly off-line as a means of comparing the efficacy of corrosion control chemical products. By this means, it was found that the water system would benefit by changing the corrosion control chemical product from a 50/50 polyphosphate/orthophosphate product to an orthophosphate product. The monitoring at the entry point and high water age locations captured the nature of the original water environment as well as the environment with the change in corrosion control product and, finally, the change in disinfection chemicals and the new steady state of water chemistry and microbiology. The new steady state showed lower lead and copper concentrations with greatly less variation than seen in


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the original system. This was confirmed by comparison to Lead and Copper Rule sampling data as well as data from three residences sampled during the monitoring station project.

Importance of Metal Transfer in Comprehensive Process Control and Vice Versa

The extra step taken to routinely measure the interaction of the water with metal surfaces is quite interesting. As stated before, this information is profoundly revealing in the mechanisms that shape the consumers’ water quality. The idea to use information on metal transfer to water for controlling consumer water quality came from work the Lead and Copper Rule compliance. In determining the mechanisms of lead and copper transfer into water, the type of chemical compounds that build up on lead and copper surfaces and the solubility of those compounds determine the concentration of lead and copper in the water (AwwaRF and DVGW-TZW 1996).

In addition, many of these chemical scales can sorb other metals as they flow past, store them, and then randomly release the metals at concentrations higher than they are typically found in the water. Metals such as lead, copper, and even radium concentrations can vary in the water because of this mechanism (Schock 2005, Maynard and Mast 2006, Schock et al. 2008).

And, it is now understood that microorganisms play a role in releasing lead and copper into water with biofilms that surround and protect the microorganisms creating localized corrosive environments (Bremer et al. 2001). Therefore, the presence and activity of non-pathogenic microorganisms must be taken into account when concerned with lead and copper transfer to water. Biostability of water becomes a focus of control, where biostability refers to the nutrients in water that encourage the growth of microorganisms balanced with disinfection available in the water to stop their growth (Van der Kooij 1992, Volk and LeChevallier 2000).

In summary, it is the water chemistry environment in the distribution system that controls the build-up of chemical scales and biofilms. In turn, chemical scales and biofilms control the release of lead and copper (and other metals) to the water. And, they determine many aspects of water quality that the consumer experiences after receiving water that has been in contact with the piping material and pipeline scales and films. Consumer water quality cannot be understood in depth unless metal transfer is studied. And, control of lead and copper corrosion and transfer to water cannot be achieved appropriately unless viewed in terms of this comprehensive water environment perspective. That is, lead and copper monitoring and control are functions of a comprehensive consumer-centric process control methodology and vice versa. PRS Monitoring Stations

The PRS Monitoring Station configuration and protocols have been described elsewhere (Cantor 2009). Briefly, the PRS Monitoring Station is based on the Water Research Foundation’s pipe loop apparatus developed in the early 1990’s to perform off-line tests on lead and copper pipe (AwwaRF and DVGW-TZW 1996). In 1997, a Process Research Solutions’ project modified the pipe loops to “mini-pipe loops” in order to be installed at various locations in the distribution system and continuously monitor the water’s metal release trends (Cantor et al. 2000). That design then evolved into the PRS Monitoring Station where instead of pipes, which are difficult to work with, metal plates stacked in a test chamber are exposed to water. There is a similar metal surface area on the plates to volume of water in the test chamber as there is in a 1.5 inch diameter pipe loop apparatus. The PRS Monitoring Station with its controlled flow, configuration, and stagnation time is an abstraction of residential sampling, especially in


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regard to the Lead and Copper Rule. Figure 2.4 shows a photo of a station as it is configured in 2011. Note that any metal, alloy, or composite metals relevant to a water system can be studied in the monitoring station by selecting the appropriate plates for the test chambers. PRS Monitoring Station Startup

The startup of the PRS Monitoring Station includes steps to insure that the water samples from the station measure only the microbiological activity in the system’s water and not that accidently introduced by the station itself or the sampling procedures. The original design of the PRS Monitoring Station has been modified so that air that enters the station at the air vent to push out the stagnation water sample is filtered with a 0.45 micron in-line air filter. See Figure 2.4. The station is also shock-chlorinated for twenty-four hours after installation and again for fifteen minutes after the metal plates are installed. Before sampling, the sample tap is wiped with alcohol or chlorine solution and the water in the tip of the sample tap is flushed before capturing the microbiological sample. NSWC made additional modifications to force the stagnation sample out by system pressure rather than by atmospheric air pressure; water is allowed to flow through sample taps whenever water flows through the station and not just while drawing a sample to keep the taps flushed with fresh water. See Figure 2.5.

The steps for starting up the monitoring stations are as follows:

1. Shock-chlorinate the PRS Monitoring Stations by installing empty test chambers, closing the station off from the influent water supply, introducing chlorine at an elevated concentration and holding the chlorinated water in the system for at least 24 hours.

2. Prepare metal plates by dipping in muriatic acid (HCl) for 10 to 20 seconds. Rinse the plates with de-ionized water and stack the plates on the pipe insertion racks with plastic spacers between the plates and a locking nut to secure them in place.

3. Drain the monitoring station of the chlorine solution and open the test chambers for the installation of metal plates.

4. Install the racks of metal plates into the test chambers. 5. Shock-chlorinate the monitoring stations with the metal plates installed, but only

for 15 minutes. This is done to disinfect the stations after being opened for the metal plate installation.

6. Rinse out the high chlorine water and begin automatically-timed flow through the stations with weekly water sample collection.

PRS Monitoring Station Operation

For regular operation, water flows through the monitoring station one hour a day at 0.5 gpm per test chamber. For the other twenty-three hours in a day, the water sits stagnant. Samples are taken from the flowing influent water at the influent sample tap. After a six hour (or at least a consistent time period) stagnation time, the test chamber sample taps are wiped with alcohol or chlorine solution, a small quantity of sample water is wasted to flush the sample tap, and then samples are taken from the test chambers where the metal plates reside. Typically, the sample for the metal is taken first and the microbiological sample is taken next. Other samples


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can be taken after that as was done to measure the change in several parameters before and after stagnation. Sample taps on the monitoring stations are described in Table 2.3 and shown in Figure 2.4.

Photo courtesy of Process Research Solutions, LLC Figure 2.4 Photo of a 2011 PRS Monitoring Station


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Figure 2.5 Adaptation of PRS Monitoring Station design at NSWC

Table 2.3 NSWC sampling sites on two PRS Monitoring Stations

Site ID Description Comment Inf 1 Water flowing into the PRS

Monitoring Station at the distribution systems’ entry point

Entry point water quality represents the highest dosages of treatment chemicals and the least interaction of the water with the piping material and pipe scales and films

1-Cu-1 Water that has stagnated in contact with copper plates at the entry point PRS Monitoring Station

The transfer of copper into water at the entry point represents some of the effects and side-effects of the water chemistry and microbiology at that point.

1-Pb-1 Water that has stagnated in contact with lead plates at the entry point PRS Monitoring Station

The transfer of lead into water at the entry point represents some of the effects and side-effects of the water chemistry and microbiology at that point.

Inf 2 Water flowing into the PRS Monitoring Station at a point of high water age in the distribution systems.

High water age water quality represents the lowest concentrations of treatment chemicals and the most interaction of the water with the piping material and pipe scales and films

2-Cu-1 Water that has stagnated in contact with copper plates at a point of high water age in the distribution systems.

The transfer of copper into water at a point of high water age represents some of the effects and side-effects of the water chemistry and microbiology at that point.

2-Pb-1 Water that has stagnated in contact with lead plates at a point of high water age in the distribution systems.

The transfer of lead into water at a point of high water age represents some of the effects and side-effects of the water chemistry and microbiology at that point.

Relocation of sample taps that run continuously during system flow.


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TOOL #3: INTERPRETATION OF WATER SYSTEM DATA Shewhart Control Charts General concepts

This project demonstrates the use of Shewhart control charts. The history, concepts, and details of Shewhart control charts are described elsewhere (Wheeler and Chambers 1992; Cantor and Cantor 2009). In general, the charts provide a quick view as to whether a system is operating the same as it was on previous days or whether new factors are acting on the system to make today’s data different. Figure 2.6 shows the basic features of a type of Shewhart control chart. A measured value is plotted over time. The average of the data points are calculated and plotted as a line around which the data vary. “Control limits” are calculated using a particular statistical formula and plotted on the chart. The control limits form a boundary within which data are expected to fall if the same factors that generate the data persist into the future. If data points fall outside of the control limits, there is most likely a different factor or factors occurring in the system than in the past. Action should be taken to determine the cause of the different behavior.

Figure 2.6 Basic features of a Shewhart control chart

A detailed look at control charts

A set of data can be described and compared to other datasets, under certain circumstances, if we can state the number of data points, the average value, and the standard deviation of the data points. However, the usual application of standard deviation is based on the assumption that the probability of individual values occurring in a dataset can be described by a Normal Curve, that is, if the dataset is “normally distributed”. The use of standard deviation is further based on the assumption that all data points are obtained in a random manner and no data point is influenced by previous data points, that is, individual values are “statistically independent”.


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In some applications of statistics, experimenters can develop random sampling procedures which may justify the use of statistical methods tied to a specific probability distribution. However, a set of observed data from a water system is not necessarily described by a single probability distribution like the Normal Distribution. Gathering water system data is like pulling products off of an assembly line for measurement, where, there can be no randomization of measurements or measurement scenarios, only the grabbing of samples over time. So, water samples are taken over time with the conditions that produced the previous sample influencing the next sample taken.

If the word, “variability” is used to replace “standard deviation”, then describing a dataset can be generalized to data that are not necessarily defined by a particular probability distribution. This was the clever approach used by Dr. Walter A. Shewart in the 1920’s in developing control charts for product assembly line data. He came up with a new measurement of variability, called a “sigma unit”, calculated in such a way that 99 to 100 percent of all data will be located within a distance of three sigma units on either side of the average for a dataset belonging to one of a number of probability distribution shapes likely to be found in practice. If a data value is observed to fall outside the range determined by the sigma units, this is a signal that the system has experienced a special cause of variation beyond the usual causes of variability.

For control charts used in this project for water system data, a particular type of control chart, called an “Individuals Chart” or a “Control Chart with Subgroup Size One”, is used. The sigma unit, defining variability of the data, is based on the calculation of the difference between successive data points. This is called a “Moving Range”. The details of calculation can be found elsewhere (Wheeler and Chambers 1992). Table 2.4 shows an example calculation where “mR” is the moving range; plus and minus 1, 2, and 3 sigma units around the average are calculated. The “UCL” (upper control limit) is equivalent to the average plus 3 sigma units; the “LCL” (lower control limit) is equivalent to the average minus 3 sigma units. The data from Table 2.4 are plotted in Figure 2.7. Besides the UCL and LCL, representing 3 sigma units, other control limits representing 1 and 2 sigma units are also drawn along with the average line. Below the Individuals Chart is its associated Range Chart where the moving range is plotted along with the average moving range and the UCL of the moving range. Discussion about the use of the Range Chart is outside the scope of this project. Control chart rules of interpretation

The boundary formed by the UCL and the LCL represent the expected variation of a measurement based on historical data with the system’s typical combination of factors working on the system. A data point that falls outside of that boundary can only get there by means of uncommon factors influencing the system. In addition to responding to data points outside of the control limits, there are other rules for using the control charts that add more sensitive detection of uncommon influencing factors (Wheeler and Chambers 1992). Based on the following list of rules with increasing sensitivity, look for the following atypical patterns:

1. Data fall outside the 3 sigma unit lines 2. At least 2 out of 3 successive values fall on the same side of the average and are 2

sigma units or greater away from the average 3. At least 4 out of 5 successive values fall on the same side of the average and are 1

sigma unit or greater away from the average


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4. 8 or more successive points fall on the same side of the average line The Shewhart control charts do not show what has caused a change to occur. As is seen

in this project, several water quality parameters can be correlated together at times, but this only shows correlation, not causation. If the atypical patterns occur, ask questions about the water system. Have any changes occurred on purpose or any unplanned events taken place? Note those changes on the control charts over the appropriate time period.

It is not true that if one of the rules is broken, there is a 100% probability that an uncommon factor is influencing the system. There is a very low probability that a false positive can occur. Such a false alarm would correspond to data points breaking any of the five rules just by chance from random noise of the system. Even with this potential for a false alarm, it is better to troubleshoot a few false alarms with low probability of occurrence than to have no guide at all as to the status of the system’s operation.

There can be false negatives. That is, something could be occurring but it is not evident on the control chart. This could happen if the information was not captured in the first place. The frequency of monitoring for each parameter must be determined so that important information is not missed. Also, the use of more than one of the rules listed above can increase the sensitivity of the control chart so that the probability of false negatives is lowered. However, the more rules used and the more sensitive the control chart, the higher is the probability of false positives.

When a data point is seen outside of the UCL or the LCL, calling this individual data point that did not meet expectations an “outlier” and removing it from the dataset is not an appropriate action when using control charts. Instead, the data point is noted on the control chart and the situation that created the odd data point is investigated in the system. In order to react to the odd data point immediately, the use and study of control charts should be incorporated into daily practices and management meetings. If the data point is thrown out, ignored, or not responded to in a timely manner, the control charts are useless.

Shewhart control charts are useful in many ways, not just for signaling atypical behavior. They lend themselves to visual guidance in improving a system. System operation can be changed to try to move the average to a desired value; system operation can also be changed to try to narrow the range between control limits, thereby narrowing the variation of a measurement and creating a more consistent process. The charts give visual feedback that desired changes are being accomplished or not.

The control charts and the use of the control limits can be modified to meet tracking and improvement goals. There are times that a person may want to restart the calculation of control chart statistics. For example, if a major system change has occurred, the data collected after the change should be separated from the data before the change with new control limits calculated. Past data can also be divided into smaller or larger time sequences and control limits re-calculated, as desired. For example, a record of data may be desired for each calendar year for annual comparisons. And, goals or regulatory limits can be imposed on any control chart as additional references to compare what the system actually produces to the goals or limits desired.

The control limits gain more meaning with more data points used in the calculation of the statistics. After 10 data points, the statistics become more representative of the system being monitored; 20 or more data points create a better foundation for these estimates of system variance.


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Table 2.4 Example calculation for an Individuals Chart (Subgroup Size One)

Example Control Chart Measured Value

Date Results mR ‐3 Sigma ‐2 Sigma ‐1 Sigma Average +1 Sigma +2 Sigma +3 Sigma Avg mR mR UCL

6/22/2011 39.0

6/23/2011 41.0 2.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

6/24/2011 41.0 0.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

6/25/2011 41.0 0.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

6/26/2011 43.0 2.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

6/27/2011 44.0 1.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

6/28/2011 41.0 3.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

6/29/2011 42.0 1.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

6/30/2011 40.0 2.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

7/1/2011 41.0 1.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

7/2/2011 44.0 3.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

7/3/2011 40.0 4.0 36.8 38.4 39.9 41.4 42.9 44.5 46.0 1.7 5.6

Average 41.4 1.7 36.8 41.4 46.0Min 39.0 0.0 LCL Average UCLMax 44.0 4.0 Count 12 11

Notes:

mR=moving range; the difference between two successive data points Sigma=measurement of variation=Average Range/1.128 where 1.128 is a value from a statistics table +3 Sigma=UCL=upper control limit of the Individuals Chart=Average + 3*Sigma -3 Sigma=LCL=lower control limit of the Individuals Chart=Average-3*Sigma Avg mR=Average Moving Range mR UCL= upper control limit of the Range Chart=3.268* Avg mR where 3.268 is a value from a statistics table

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Figure 2.7 Individuals Chart and associated Range Chart from Table 2.4 data


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This project demonstrates that the use of control charts is an empirical approach to water quality and water system operation. That is, we respond to our observations of the system, using data to guide engineering and management actions. Science has not yet provided explanation and prediction of the multitude of complex interactions that occur in a water distribution system. In the meantime, we can take off the blinders of “unknowingness” and use this empirical approach to guide operational decisions and to help improve scientific and management theories. Project Data Analyses

Data from this project are presented in various ways. Data are plotted on Shewhart control charts. These charts are not only important for the control chart statistics and control lines, but viewing the data plotted over time is an essential step in data analysis.

Summary displays of the Shewhart control chart statistical parameters have been created to compare several charts at a glance. The summary displays show the average value and the control limit values for each scenario to determine if the ranges of variability overlap or not, marking a significant similarity or difference between scenarios. This distillation of the datasets eases comparisons, but one must not forget to return to the actual charts to view the data trends over time and the interplay of the data with the control limits.

There are also attempts to study relationships between water quality parameters using linear correlation matrices, and scatterplot matrices where non-linear patterns can also be seen if they exist. Linear correlations use a method of determining the equation of a line that the data points appear to center around. This method does not depend on a dataset’s probability distribution as was discussed earlier in the context of control charts; it is merely a numerical method to force a linear pattern on a dataset and then determine how close the data come to actually forming that pattern. The outcome of the method determines the slope and y-axis intercept of a line from the data points. The method also yields a correlation coefficient. If the square of the correlation coefficient, R2, equals 1, then the data points all fall on the line; if R2 = 0, there is no linear pattern that can be discerned. Otherwise, R2 is a fraction between 0 and 1indicating the fit of the dataset to a line.

Computer software has been used to manage and analyze this large amount of data. My Monitoring Data® (Process Research Solutions, LLC; Madison, WI) is used for data input, storage, retrieval, graphing, and production of Shewhart control charts. QI Charts (Process Improvement Products; Austin, TX) is used for production of Shewhart control charts. Minitab® 16 (Minitab, Inc.; State College, PA) is used for production of Shewhart control charts, linear correlation matrices, and scatterplot matrices. Microsoft Excel® (Microsoft; Bellevue, WA) is used for data transfer, organization, graphing, and interaction with the other software. Note that the software to produce Shewhart control charts that comes with this report is a Microsoft Excel® add-in and only requires Excel® 2007 for use.

ADDITIONAL INFORMATION

As described previously, the project’s monitoring strategy encompassed factors known to influence water quality in distribution systems. However, the possibility of unknown factors at work on the water system was also considered. Additional information was collected and evaluated to test any conclusions drawn from considering the known factors and to look for other possible trends.


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Data from Previous Distribution System Monitoring Projects

Data from the previous NSWC project are used for comparison in evaluating this project’s results from the PRS Monitoring Stations. Data from two other Wisconsin water systems (Waukesha and Onalaska) where the PRS Monitoring Stations have been implemented are also used in this project for comparison. In addition, data from monitoring and testing apparatuses that were the precursors of the PRS Monitoring Station are used for comparison. The early apparatuses were used at the Wisconsin water utilities of Madison, Janesville, Monona, Dane, and Lone Rock by Process Research Solutions, LLC (Cantor et al. 2000, Cantor et al. 2003).

Examination of Metal Plates

After twelve months of exposure to NSWC water, the metal plates in the two monitoring stations were removed and sent to two laboratories for analysis. The analyses performed on the metal plates are equivalent to those that would be performed on pipe harvested from the distribution system. These analyses determine which chemicals in the water are key players in dissolved and particulate metal release from pipe walls. An assessment of biofilms on the metal surface also paints a picture of distribution system water quality.

Each monitoring station has two test chambers, one filled with lead plates and one filled with copper plates. Each test chamber has two racks of metal plates, each rack with eight plates. The plates measure 2.5 inches by 2.5 inches, 1/16 inch thick. One rack of plates from each of the four test chambers were delivered to the Wisconsin State Laboratory of Hygiene (WSLH) for microbiological analysis of surface biofilms about three hours after removal from the monitoring stations. Immediately upon arrival at WSLH, the metal plates were processed for removal of biofilms into sterile reagent water using both a “sonicator” and a “wrist-shaker”, a dual technique developed previously for PRS Monitoring Station metal plates (Cantor 2009). Figure 2.8 shows a photo of the metal plates in the “wrist-shaker”.

The suspension of biofilms in the reagent water was used for microbiological analyses for HPC_R2A, iron bacteria, and sulfate-reducing bacteria. A portion of the suspension was given to another laboratory at the University of Wisconsin in Platteville for MGI analysis. Two of eight metal plates from each test chamber were also given to the University of Wisconsin laboratory for inspection by scanning electron microscope. Refer to Appendices A, B, and C for details of microbiological analyses.

The other rack of plates from each of the four test chambers were shipped to the University of Cincinnati for metallurgical analysis of surface scales. The plates are examined by various methods including X-Ray Diffraction, X-Ray Fluorescence, scanning electron microscope, and Raman scattering as has been applied previously to PRS Monitoring Station metal plates (Cantor 2009). Refer to Appendix D for details of analysis for this project.


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Figure 2.8 Removal of biofilms from PRS Monitoring Station metal plates

Residential Sampling

During the twelve months of monitoring station operation in this project, water at four residences was sampled in March, July, and October of 2010. Sampling was performed by NSWC personnel. Each time, the residence was first visited to draw fresh water to the kitchen sink faucet being used as a sample tap. Water analyses were performed on the fresh flowing water similar to the analyses performed on the monitoring stations’ influent water. Then, the water in the residence was allowed to stagnate for a minimum of six hours. After that time period, a first-draw one liter stagnation sample was taken for analysis of metals, then, a stagnation sample was drawn for microbiological analysis. Finally, samples for monochloramine, free ammonia, nitrite, and nitrate were taken to document the change in those parameters in the stagnating water in comparison to the flowing influent water. The stagnation water analyses also paralleled those at the monitoring stations. This protocol is similar to that prescribed by the Lead and Copper Rule (CFR 40:141:I) except that trained personnel have performed the sampling in this project and more water analyses are performed than the Rule requires.

Lead and Copper Rule Sampling

The Lead and Copper Rule of the national primary drinking water regulations was the initial motivating force behind developing the sampling protocols and the PRS Monitoring Station. The Rule requires a water utility to periodically sample residences in the distribution system. At the residences, water must stagnate for a minimum of six hours as a reaction time between metal pipes and adjacent water. After that, first-draw one-liter water samples are obtained and analyzed for total lead and total copper. A water utility is in compliance with the Rule if 90 percent of the samples show lead to be at 15 µg/L or less and copper to be at 1300 µg/L or less.


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There are a number of issues with Lead and Copper Rule sampling revolving around obtaining representative samples from the distribution system. Individual residences have various water usage patterns and piping configurations, so it is difficult to determine if resultant lead and copper concentrations were caused by system-wide factors or by localized factors. Untrained property owners take the Lead and Copper Rule samples, so there are sometimes questions as to the integrity of the data. Nevertheless, conclusions about Lead and Copper Rule compliance are drawn from these data. Past Lead and Copper Rule sampling data from the NSWC system are used in this project to compare to the PRS Monitoring Station data. SCADA Data

The center of a modern water system’s data collection and assessment is its SCADA system. In this project some water flow and chemical feed data from the SCADA system were used to demonstrate the use of Shewhart control charts on data other than water quality monitoring data. Any measurement over time can be used in a Shewhart control chart with benefits gained, in many cases, from its visual guidance on average values and variation of values. The control charts of the SCADA data were also used in comparison to the water quality monitoring results. On-line Sensor Data

Some water quality data come into the SCADA system from on-line water quality sensors producing a continuous stream of data. Turbidity data at the water treatment plant was used in this project to demonstrate the use of Shewhart control charts with on-line sensor data. Regulatory and Investigative Data

Water utilities must comply with the various drinking water regulations. A lot of water quality monitoring is performed in order to fulfill the regulatory requirements. In many cases, the regulatory data are sent to the regulatory agency to show compliance but are not utilized at the water utility for insight into the system.

For example, water utility personnel must visit select sites throughout the distribution system to take water samples for Total Coliform analysis monthly. At the same time, they must take a measurement of the disinfection concentration at those sites. The disinfection data can be used to plot a history of disinfection over time at each site and to pinpoint areas of the distribution system where disinfection is low. It can be assumed that where disinfection concentration is low, microorganisms have a greater probability of thriving, even if pathogenic microorganisms are not found at that location. If microorganisms can thrive, then there is a greater probability that water quality in some aspects will be lower.

Since visits are made to those sites routinely anyway, other telltale water quality parameters can be obtained at the same time. Turbidity is a water quality parameter that is relatively inexpensive to measure and can indicate changes in source water quality, treated water quality, or distribution system cleanliness. NSWC has been collecting turbidity data at the Total Coliform Rule sampling sites since January 2010.

The NSWC Total Coliform Rule sampling site disinfection and turbidity data have been plotted on Shewhart control charts for each regulatory sampling site.


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CHAPTER 3 RESULTS

MONITORING STATION DATA

Two PRS Monitoring Stations were used in the water systems of the North Shore Water Commission to collect data for this study. Sample taps on the monitoring stations are described in Table 2.3. One station was located at the water treatment plant, drawing treated water before it entered the distribution systems. This monitoring station is referred to as the Entry Point (EP) Monitoring Station or Monitoring Station 1. The station included a test chamber of lead plates and a second test chamber of copper plates. The lead plate test chamber is referred to as 1-Pb-1; the copper plate test chamber is referred to as 1-Cu-1.

The second monitoring station was installed at a location of high water age in one of the distribution systems. The location was in the basement of a house. It should be noted that the piping up to the monitoring station included a length of galvanized iron pipe as well as a copper water service line. This monitoring station is referred to as the High Water Age (HWA) Monitoring Station or Monitoring Station 2. The station included a test chamber of lead plates and a second test chamber of copper plates. The lead plate test chamber is referred to as 2-Pb-1; the copper plate test chamber is referred to as 2-Cu-1.

Besides sampling stagnating water from the test chambers, flowing influent water to each monitoring station was sampled. Influent samples are designated as “Inf” with further designation of 1 or 2 to identify the monitoring station.

The data are presented here in two groups. In the first group, the monitoring stations’ data show the resultant lead concentration, copper concentration, and microbiological activity that come from stagnating water in the presence of the metal plates. These are considered variables dependent on the conditions in the water that flowed into the test chambers before the stagnation began. Therefore, the second group of data, the independent variables, is the water quality parameters in the influent water to the monitoring stations.

Lead and copper concentration data from the test chambers in the first month of monitoring station operation have been found to be uncharacteristically high in this and previous studies. In this study, the first month of data has been separated out on the control charts.

During the twelve months of monitoring, the dosage of orthophosphate, added to control lead and copper concentrations, was lowered by about 0.07 mg/L as PO4 for a brief time period to determine if the dosage could be notched down and the chemical eventually removed from the system all together. The dates of such changes are shown in Table 3.1. Where relevant, the control charts are divided into these phases.

Table 3.1 Phases of orthophosphate dosage changes Phase Begin Data End Date Description

1 11/1/2009 11/31/09 Disregard data from first month of station operation 2 12/1/2009 5/15/2010 Initial dosage 3 5/16/2010 7/22/2010 Dosage lowered 4 7/23/2010 11/15/2010 Dosage raised back up


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Lead, Copper, and Microbiological Activity

Control charts and comparative summary charts for lead concentration, copper concentration, and microbiological activity from the test chambers of the monitoring stations are shown in Figures 3.1 to 3.6.

The lead concentration in the water influent to the monitoring stations is routinely measured. In Figure 3.1, it is seen that the influent lead concentration is insignificant at both monitoring stations (Inf 1 and Inf 2). The water is then made to stagnate adjacent to lead plates in the test chambers. At the entry point station (Monitoring Station 1), the resultant average total lead concentration is around 60 µg/L. Because there is no significant lead concentration flowing into the station, all of the lead in the test chamber water comes from the chamber’s lead plates. The dissolved lead fraction of the total lead concentration is above 80%. This means that lead coming from the metal plates is mostly in dissolved form and there is very little lead in particulate form.

Meanwhile, at the high water age station (Monitoring Station 2), the resultant average total lead concentration stays higher than at the entry point station. In addition, about 50% of the lead is in particulate form as opposed to being mostly dissolved lead at the entry point. Another difference between the two stations is that the total lead increases at the high water age station during the third phase of the project as defined in Table 3.1.

Figure 3.2 gives another view of this information. Here, data from each sampling site on the monitoring stations and each project phase are represented by three points from each of the control charts in Figure 3.1. One point for each scenario in Figure 3.2 is the average lead concentration that has been shown as a light solid line on the corresponding control chart in Figure 3.1. The two other points for each scenario in Figure 3.2 are the boundaries of the observed lead concentration as shown by light dashed lines on the corresponding control chart in Figure 3.1. That is, these points are the values of the upper and lower control limits, showing the expected variability of the dataset. The monitoring station sampling sites are designated in Figure 3.2 on the x-axis along with the type of lead measurement (T=total lead; D=dissolved lead) and the orthophosphate dosage phase (2, 3, or 4) from Table 3.1. While lacking a view of data trends and patterns that is provided by the control charts in Figure 3.1, this representation in Figure 3.2 gives a quick view of each scenario’s expected concentration range to compare to the other scenarios.

Again, it is shown that influent lead is insignificant so that any lead we see from the test chambers came from the metal plates. Total lead is higher at the high water age location than at the entry point. The particulate fraction of lead is also higher at the high water age location. The total lead at the entry point very slightly decreases over the phases, seemingly unaffected by the different scenarios. If one looks closely, the dissolved lead at the entry point might be slightly increased during Phase 3, but that may not be significant. On the other hand, the high water age total lead concentration increases greatly in Phase 3, seemingly pushed up by an increase in dissolved lead.

Can it be assumed that because the orthophosphate dosage was lowered, the dissolved lead increased at the high water age station? No. That is the first lesson to be learned from studying this plethora of water system data. One must be very careful not to assign “causation” when a response may just be a coincidental “correlation”. This question as to the effect of the orthophosphate dosage change will be carried throughout the report.


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Similarly, Figures 3.3 and 3.4 display the findings for copper concentration. For the entry point station, influent copper is insignificant. At the high water age station, some very low concentration of dissolved copper between 0 and 30 µg/L enters the test chambers of copper plates. This most likely occurs because the monitoring station is located in the basement of a house where the influent water can be influenced by the water service line and the basement plumbing. Data from the entry point station copper plate test chamber show copper to steadily decrease over the project no matter what the orthophosphate dosage phase with copper over 90% in the dissolved form. Taking into account copper added from the influent water, the copper concentration released from the copper plate test chamber at the high water age station is about the same as the entry point or a little higher. Copper is also mostly in dissolved form at the high water age station. There is also a pattern of decreasing copper over time, but the decrease is stalled in Phase 3.

The decreasing copper pattern is curious in that it could represent a time of passivation of the new copper plates that were installed in the monitoring station at the beginning of the monitoring project. This issue will be discussed later after all of the data related to this project are presented.

Figures 3.5 and 3.6 describe the microbiological activity in the monitoring stations. The limitations of using heterotrophic plate count (HPC_R2A) as an indicator of microbiological activity were pointed out earlier in this report. Nevertheless, it is an inexpensive and readily available tool that is used in this project with caution. HPC_R2A data was gathered from the influent water and both the lead and copper test chambers at both monitoring stations. All charts show an upward trend to the presence of microorganisms over the project period but the patterns per monitoring station sampling site vary.

At the entry point station, the microbiological activity in the water of the lead test chamber is higher than that in the influent water indicating growth occurring in the test chamber. During Phase 3, the average HPC drops but also becomes more variable, increasing the range within which the HPC is found. The Phase 4 data pattern returns to that seen in Phase 2 before the Phase 3 perturbation. The microbiological activity in the water of the copper test chamber is very curious as it stays low during Phase 2 and then jumps greatly in Phase 3, remaining just above that level in Phase 4.

At the high water age station, the HPC is higher in the influent water than in the influent water at the entry point station. In Phase 3, the HPC makes a larger jump up than the change seen in the entry point station influent water. The HPC in the water from the lead test chamber is higher than the influent water HPC; there is an odd narrowing of variability during Phase 3. On the other hand, the copper test chamber HPC is the same or slightly higher than the influent water with a similar pattern over time. Influent Conditions

A number of water quality parameters were tracked in the influent water in order to describe the chemical and microbiological environment to which the metal plates in the test chambers were exposed. The significance of each water quality parameter has been discussed in the Methods section of this report.

It was previously known that certain parameters describing general water “type” (alkalinity, hardness, and total dissolved solids) do not vary significantly in the NSWC system so


40

those general parameters were collected twice during the study, once during colder temperatures and once during warmer temperatures. Table 3.2 lists the results.

Figure 3.1 Control charts: monitoring stations’ lead concentrations


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Figure 3.2 Summary charts: monitoring stations’ lead concentrations


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Figure 3.3 Control charts: monitoring stations’ copper concentrations


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Figure 3.4 Summary charts: monitoring stations’ copper concentrations


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Figure 3.5 Control charts: monitoring stations’ HPC concentrations


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Figure 3.6 Summary charts: monitoring stations’ HPC concentrations

Table 3.2 NSWC general water quality parameters

Sample Date

Item Measured Entry Point

Influent (Inf 1) High Water Age Influent (Inf 2)

Units

11/12/2009 Alk, tot 104 104

mg/L as CaCO3

8/18/2010 Alk, tot 104 103

mg/L as CaCO3

11/12/2009 Ca 35 35 mg/L 8/18/2010 Ca 35 35 mg/L 11/12/2009 Hard, tot

130 130 mg/L as CaCO3

8/18/2010 Hard, tot 130 130

mg/L as CaCO3

11/12/2009 Mg 12 12 mg/L 8/18/2010 Mg 12 12 mg/L 11/12/2009 TDS 170 160 mg/L 8/18/2010 TDS 160 160 mg/L


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However, other water “type” parameters (pH and temperature) do vary frequently. Figures 3.7 and 3.8 show the variation of pH and temperature during the project. The control charts show the data trends over time, the summary charts compare typical ranges of the data between entry point monitoring station influent conditions and high water age monitoring station influent conditions. The summary chart of pH values indicates that the average pH at the high water age location is slightly higher than that at the entry point, but the ranges of variation of both points greatly overlap. It can be concluded that the pH at the two locations is essentially the same within the precision that can be measured.

Temperature, of course, is greatly affected by seasonal changes. The temperature at the high water age location is the same or higher than that at the entry point, depending on the season.

Disinfection in the water is measured in terms of total chlorine concentration and in terms of monochloramine. Monochloramine is the active fraction of the total chlorine that provides the disinfection power. The remaining fraction of total chlorine includes compounds of chlorine that do not participate in disinfection. The goal is to maximize the monochloramine fraction. This is done with performing a delicate balance of the initial chorine to ammonia ratio of the added chemical products and the pH of the water.

There is also the loss of chlorine and monochloramine between the entry point and the high water age location to be concerned about. This loss reflects the cleanliness of the pipelines and time that water spends in the pipelines, where the chlorine reacts with pipeline scales and films and biofilms. The cleaner and fresher the water system, the closer the high water age chlorine or monochloramine concentration is to that at the entry point. The “loss of disinfection” graphs of Figures 3.9 and 3.10 show that 70 to 75 percent of the disinfection added at the entry point reaches the high water age location.

Disinfection is added to the water to prevent the growth of microorganisms in the distribution system. The amount of disinfection chemicals added needs to balance factors in the water that encourage the growth of microorganisms. A significant factor that encourages the growth of microorganisms is nutrients available in the water, especially nitrogen, phosphorus, and carbon.

As discussed in the Methods section, ammonia, a nitrogen compound, can be released from the disinfection compound, monochloramine. This free ammonia released in the distribution system is a nutrient for microbiological growth. The phenomena of “nitrification”, the use of ammonia by microorganisms to produce nitrite and then nitrate, must be tracked and prevented in water systems that use chloramine disinfection. Free ammonia was tracked in the influent water to the monitoring stations as its presence provides the environment for nitrification to occur. Figure 3.11 shows an insignificant presence of free ammonia at the entry point monitoring station influent. At the high water age monitoring station influent, ammonia has been released. It is still at a very low concentration. Figure 3.12 emphasizes that there is insignificant concentration of free ammonia at the entry point and that release occurs at a later time in the distribution system.

To track nitrification, the change in ammonia, nitrite, and nitrate between the water influent to the monitoring stations and the water that has stagnated in the monitoring stations’ test chambers was tracked. Tables 3.3 to 3.6 show the results where a positive number indicates an increase after stagnation and a negative number indicates a decrease. Changes greater than the Shewhart control chart precision of the analytical method are marked in bold. (See Methods and Materials: Field and Utility Laboratory Analyses.) It can be concluded that ammonia (NH3)


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does increase from the degradation of monochloramine; sometimes it can show a decrease from utilization by microorganisms. Any changes in nitrate (NO3) and nitrite (NO2) are very small. Nitrification does not appear to be significant.

The loss of disinfection (monochloramine) between the flowing and stagnating water is another gauge of microbiological activity as the disinfection will decrease during stagnation to counteract microbiological growth. Tables 3.3 to 3.6 also show the loss of monochloramine.

Assimilable organic carbon (AOC) described in the Methods section, represents carbon in a form readily available as a nutrient source for microorganisms. Being a very expensive analysis, the influent water to the monitoring stations was tested only twice during the project – once in cold weather and once in warm weather. Table 3.7 shows the results along with previous data and comparative data from other water systems. It was no surprise that AOC is found to be higher at the high water age location than the entry point location. A certain concentration of AOC comes in with the source water. It is also created in some water treatment processes where larger carbon compounds are broken into smaller compounds, making the carbon more accessible to microorganisms (Escobar and Randal 2001). But, AOC also comes from the accumulation of dead microorganisms. The sloughing of non-living biofilm material into water from distribution system pipes increases the AOC in the distribution system. It was a surprise to see a lower AOC concentration in the warmer month of August rather than in the colder month in November. Typically, source water AOC and distribution system AOC concentrations increase with increasing temperature. It would be nice to be able to follow the AOC throughout the year, but the analyses are just too expensive to have this luxury.

Phosphorus is another nutrient for microbiological growth. Figure 3.13 shows the orthophosphate addition as measured at the influent sampling tap to the two monitoring stations. There is a dramatic pattern at the entry point between January and March of 2010. This reflects an operational issue. The injection of orthophosphate in the water treatment plant process train was problematic; a precipitate was forming near the injection point. In moving the injection point, the entry point monitoring station began receiving water that had not had the orthophosphate introduced even though orthophosphate was being received in the distribution system and at the high water age monitoring station.

As discussed previously, the dosage of orthophosphate was lowered in May 2010 to begin a test to wean the water system off of the chemical. Figures 3.13 and 3.14 show that the orthophosphate concentration at the entry point was dropped from an average of about 0.55 mg/L as PO4 to about 0.48 mg/L as PO4. This is a very low dosage drop as it was desired to slowly notch down the concentration. At the high water age location, this drop was reflected except for one odd data point showing an increase in concentration. This could either be an actual occurrence or an analytical glitch. At the same time that the orthophosphate dosage adjustment was made, the lead concentrations from the high water age monitoring station test chambers increased but the entry point monitoring station lead did not. Erring on the side of caution, the orthophosphate dosage was increased back to the original concentration or a little higher by the end of July and the process experiment was ended for this project period. The results of this experiment will be explored later in this report.

Figures 3.13 and 3.14 display the trends for orthophosphate concentration. The slight decrease in concentration in Phase 3 is seen.


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Figure 3.7 Control charts: influent pH and temperature


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Figure 3.8 Summary charts: influent pH and temperature


50

Figure 3.9 Control charts: influent disinfection


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Figure 3.10 Summary charts: influent disinfection


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Figure 3.11 Control charts: influent free ammonia

Figure 3.12 Summary charts: influent free ammonia


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Table 3.3 NSWC change in parameters during stagnation: Entry Point Lead Test Chamber

Date ΔMono ΔNH3 ΔNO2 ΔNO3

12/29/2009 -0.26 0.00 0.00 0.03 1/7/2010 -0.64 0.02 0.00 0.02 1/14/2010 1/21/2010 -0.23 0.01 0.00 -0.01 1/28/2010 2/5/2010 -0.29 0.02 0.00 0.00 2/11/2010 2/18/2010 -0.07 0.00 0.00 0.00 2/25/2010 3/5/2010 -0.21 0.00 0.02 0.13 3/11/2010 3/18/2010 -0.27 0.16 0.00 -0.16 3/26/2010 4/1/2010 -0.28 0.03 0.00 -0.03 4/8/2010 4/15/2010 -0.34 0.00 0.00 0.07 4/23/2010 4/29/2010 -0.43 0.07 0.00 0.185/13/2010 -0.38 0.19 0.00 -0.02 5/20/2010 5/27/2010 -0.19 0.16 0.00 0.09 6/3/2010 6/10/2010 6/18/2010 6/24/2010 -0.36 0.01 0.00 0.00 7/2/2010 7/9/2010 -0.20 0.01 0.00 0.10 7/15/2010 7/22/2010 -0.20 0.03 0.00 -0.20 7/30/2010 8/5/2010 -0.36 0.00 9/2/2010 -0.30 0.00 0.00 0.19 9/9/2010 9/20/2010 -0.15 0.00 0.00 0.13 9/23/2010 10/4/2010 10/7/2010 10/18/2010 -0.31 0.01 0.00 0.00 10/21/2010 11/1/2010 -0.35 -0.02 0.00 0.00 11/8/2010 11/15/2010

Est. Shewhart Control Chart Precision 0.15 0.01 0.01 0.17


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Table 3.4 NSWC change in parameters during stagnation: Entry Point Copper Test Chamber

Date ΔMono ΔNH3 ΔNO2 ΔNO3 11/24/2009 0.08 0.01 0.00 12/3/2009 12/10/2009 -0.63 0.04 0.01 0.16 12/17/2009 12/23/2009 12/29/2009 -0.52 0.10 0.00 0.271/7/2010 -0.32 0.00 0.00 0.13 1/14/2010 1/21/2010 -0.61 0.12 0.00 0.03 1/28/2010 2/5/2010 -0.54 0.08 0.00 0.00 2/11/2010 2/18/2010 -0.26 0.00 0.00 0.01 2/25/2010 3/5/2010 -0.38 0.05 0.02 0.13 3/11/2010 3/18/2010 -0.43 0.05 0.00 -0.16 3/26/2010 4/1/2010 -0.48 0.08 0.00 -0.02 4/8/2010 4/15/2010 -0.51 0.05 0.00 0.03 4/23/2010 4/29/2010 -0.38 0.03 0.00 -0.16 5/13/2010 -0.46 0.06 -0.07 -0.04 5/20/2010 5/27/2010 -0.33 -0.01 0.00 0.07 6/24/2010 -0.41 0.04 0.00 -0.01 7/2/2010 7/9/2010 -0.28 0.04 -0.01 0.10 7/15/2010 7/22/2010 -0.31 0.07 0.00 -0.20 7/30/2010 8/5/2010 -0.28 0.04 9/2/2010 -0.34 0.01 0.00 0.18 9/9/2010 9/20/2010 -0.31 0.00 0.00 0.12 10/18/2010 -0.36 0.04 0.00 0.00 10/21/2010 11/1/2010 -0.34 -0.02 0.00 0.00 11/8/2010 11/15/2010



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Table 3.5 NSWC change in parameters during stagnation: High Water Age Lead Test Chamber

Date ΔMono ΔNH3 ΔNO2 ΔNO3 11/24/2009 -0.03 0.00 -0.2612/3/2009 12/10/2009 -0.62 -0.06 0.01 -0.07 12/17/2009 12/23/2009 12/29/2009 -0.27 0.02 0.00 0.29 1/7/2010 -0.23 -0.07 0.05 0.02 1/14/2010 1/21/2010 -0.36 -0.04 0.00 -0.03 1/28/2010 2/5/2010 -0.36 0.09 0.00 0.03 2/11/2010 2/18/2010 -0.11 -0.03 0.00 -0.01 2/25/2010 3/5/2010 -0.43 0.08 0.02 0.14 3/11/2010 3/18/2010 -0.22 0.04 0.00 -0.14 3/26/2010 4/1/2010 -0.30 0.10 0.00 0.03 4/8/2010 4/15/2010 -0.39 0.04 0.00 0.03 4/23/2010 4/29/2010 -0.44 0.10 0.00 -0.17 5/13/2010 -0.30 0.03 0.00 -0.03 5/20/2010 5/27/2010 -0.57 0.03 0.00 0.07 6/24/2010 -1.17 0.12 0.00 -0.06 7/2/2010 7/9/2010 -0.39 0.01 0.00 0.03 7/15/2010 7/22/2010 -0.46 0.14 0.00 -0.28 7/30/2010 8/5/2010 -0.38 -0.08 0.00 0.18 9/2/2010 -0.29 0.02 0.00 0.17 9/9/2010 9/20/2010 -0.21 -0.05 0.00 0.13 10/18/2010 -0.31 0.02 0.00 0.00 10/21/2010 11/1/2010 -0.19 -0.11 0.00 0.00 11/8/2010 11/15/2010



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Table 3.6 NSWC change in parameters during stagnation: High Water Age Copper Test Chamber

Date ΔMono ΔNH3 ΔNO2 ΔNO3

11/24/2009 -0.01 0.00 0.00 12/3/2009 12/10/2009 -0.44 -0.06 0.03 0.12 12/17/2009 12/23/2009 12/29/2009 -0.50 0.07 0.01 0.00 1/7/2010 -0.22 -0.07 0.05 0.26 1/14/2010 1/21/2010 -0.28 0.00 0.00 0.02 1/28/2010 2/5/2010 -0.59 0.13 0.00 0.01 2/11/2010 2/18/2010 -0.30 0.01 0.00 0.00 2/25/2010 3/5/2010 -0.35 0.07 0.02 0.14 3/11/2010 3/18/2010 -0.36 0.13 0.00 -0.14 3/26/2010 4/1/2010 -0.44 0.12 0.00 0.00 4/8/2010 4/15/2010 -0.57 -0.01 0.00 0.02 4/23/2010 4/29/2010 -0.42 0.08 0.00 -0.17 5/13/2010 -0.39 0.05 0.00 -0.01 5/20/2010 5/27/2010 -0.71 0.07 0.00 0.02 6/24/2010 -0.98 0.09 0.00 -0.03 7/2/2010 7/9/2010 -0.73 0.14 0.00 -0.02 7/15/2010 7/22/2010 -0.84 0.22 0.00 -0.28 7/30/2010 8/5/2010 -0.64 -0.06 0.00 0.15 9/2/2010 -0.54 0.09 0.01 0.15 9/9/2010 9/20/2010 -0.48 -0.05 0.00 0.13 10/18/2010 -0.46 0.22 0.00 0.00 10/21/2010 11/1/2010 -0.31 -0.11 0.00 0.00



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Table 3.7 NSWC assimilable organic carbon (µg/L as acetate-C)

Utility Water Source Sample Date Distribution System Entry Point

High Water Age Location

NSWC Lake Michigan 4/17/2008 150 12/5/2008 40 11/12/2009 140 180 8/18/2010 80 98

Milwaukee Lake Michigan 8/26/2003 130 Sturgeon

Bay Shallow wells

2/24/2009 80 70

Figure 3.13 Control charts: influent orthophosphate

Figure 3.14 Summary charts: influent orthophosphate


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Another chemical added to the water is alum (aluminum sulfate) used to coagulate particles in water before filtration, thereby making the solid material approach the filter in larger masses for easier filtration. Both aluminum and sulfate can play a role in mechanisms that release lead and copper to water. Figures 3.15 and 3.16 show that aluminum increases throughout the test period while sulfate concentration stays the same or slightly decreases. One would think that the two should follow the same pattern. But, both compounds can combine with other chemicals in the water and precipitate out in the distribution system. For example, sulfate has been observed to associate with corrosion scales and films on iron pipe.

Chloride (Figures 3.17 and 3.18) is another chemical of concern that can possibly affect the release of lead and copper into water. Except for some initial higher concentrations in the monitoring stations’ influent water, the chloride averages about 16 mg/L throughout the project. The chloride is a component of the source water and is not increased by any water treatment process. The average concentration is slightly lower at the high water age location, probably because of some precipitation in the distribution system pipes.

As discussed in the Methods section, the Chloride to Sulfate Mass Ratio (CSMR) has gained interest regarding the release of lead and copper considerations because of recent research which hypothesizes that a ratio which favors chloride (ratio>0.5) translates to higher lead release by galvanic corrosion. The CSMR (Figures 3.17 and 3.18) varied between 0.55 and 0.75 throughout the project. The CSMR was similar at both the entry point and high water age locations. The effect of CSMR on galvanic corrosion was not studied in this project.

Besides aluminum as a pipe wall scale that can sorb passing metals, manganese and iron scales function similarly in pipelines. There is insignificant manganese and iron in NSWC’s source water. However, iron does accumulate in the water system because of corrosion of iron piping system components. Figure 3.19 shows that most of the iron found in the water system is in particulate form which suggests particulate corrosion products. There are three data points with increased dissolved iron which are curious. The field filtration technique is suspected of causing this erroneous view as the fractioning of iron for analysis is physically difficult as explained previously in the Methods section. Figure 3.20 shows that total iron is higher at the high water age location compared to the entry point location. Particulate iron is more pronounced at the high water age location as well. This makes sense in that there are more iron components to corrode in the distribution system and it is harder to clean out the accumulation of iron scales in the pipelines.

There are possibly many more chemicals that should be tracked in the influent that represent source water and pipeline scales that can sorb, store, and randomly release lead and copper and other metal contaminants. An overall indicator of scales, or rather, particulates, in water is turbidity. Figures 3.19 and 3.20 show that there is very low turbidity in the influent to the entry point monitoring station. In contrast, the high water age monitoring station influent exhibits relatively high turbidity. There is an interesting pattern of turbidity falling from January 2010 through August 2010 when it begins an upward climb. The pattern parallels that of total and particulate iron.


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Figure 3.15 Control charts: influent aluminum and sulfate


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Figure 3.16 Summary charts: influent aluminum and sulfate


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Figure 3.17 Control charts: influent chloride and CSMR


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Figure 3.18 Summary charts: influent chloride and CSMR


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Figure 3.19 Control charts: influent iron and turbidity


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Figure 3.20 Summary charts: influent iron and turbidity


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Correlation of Influent Conditions on Lead, Copper, and Microbiological Activity

The correlation of influent conditions on lead concentrations, copper concentrations, and microbiological activity in the monitoring station test chambers is studied using matrices of correlation coefficients and scatterplots. Tables 3.8 to 3.11 and Figures 3.21 to 3.24 show these matrices for:

The entry point monitoring station lead test chamber The high water age monitoring station lead test chamber The entry point monitoring station copper test chamber The high water age monitoring station copper test chamber

Correlations must be interpreted with caution. There are quite a number of factors that

have been tracked in the influent water but there are probably more factors that were not accounted for. In addition, the synergy of factors cannot be discerned by these simple linear correlations. Therefore, do not confuse the correlation of two variables with the idea that one variable influenced the other variable. That is, keep in mind that correlation does not imply causation.

In reading a correlation matrix, a linear correlation coefficient sits at the intersection of any two variables. Coefficients close to +1 indicate a high degree of linear correlation where one variable increases with the increase in the second variable. Coefficients close to -1 indicate a high degree of linear correlation where one variable increases with the decrease in the second variable. These same relations can be viewed on the scatterplot matrix at the intersection of the same two variables. The scatterplot matrix will also show if some other pattern besides linear is created by comparing two variables.

All of the parameters studied in this project are shown in the heading of the correlation coefficient matrices with the last two columns being the resultant lead or copper concentration and microbiological activity as represented by HPC_R2A analysis. While correlations between the influent parameters are interesting, of special interest is the correlation of the resultant data with influent parameters.

Some parameter names have lines through them. This is to indicate that they are not included on the scatterplot matrices. Five such parameters are:

Free ammonia in the influent water (Ammonia) The change in monochloramine concentration between the influent water that

flowed into the test chamber and that concentration after six hours stagnation (Mono delta)

The change in free ammonia concentration between the influent water that flowed into the test chamber and that concentration after six hours stagnation (NH3 delta)

The change in nitrite concentration between the influent water that flowed into the test chamber and that concentration after six hours stagnation (NO2 delta)

The change in nitrate concentration between the influent water that flowed into the test chamber and that concentration after six hours stagnation (NO3 delta)


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Table 3.8 Correlation matrix: Entry Point Monitoring Station Lead Test Chamber

66


67

Figure 3.21 Scatterplot matrix: Entry Point Monitoring Station Lead Test Chamber

67


68

Table 3.9 Correlation matrix: High Water Age Monitoring Station Lead Test Chamber

68


69

Figure 3.22 Scatterplot matrix: High Water Age Monitoring Station Lead Test Chamber

69


70

Table 3.10 Correlation matrix: Entry Point Monitoring Station Copper Test Chamber

70


71

Figure 3.23 Scatterplot matrix: Entry Point Monitoring Station Copper Test Chamber

71


72

Table 3.11 Correlation matrix: Entry Point Monitoring Station Copper Test Chamber

72


73

Figure 3.24 Scatterplot matrix: High Water Age Monitoring Station Copper Test Chamber

73


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These concentrations were found to be very low with very low changes from before and after stagnation. The concentrations were so low that they were difficult to measure. Most changes in nitrite and nitrate were less than the precision of the analytical method itself. Correlation coefficients between these changes and the resultant parameter data as well as the other influent data were very low.

Total chlorine data were eliminated from the scatterplot matrices because monochloramine is related and has similar patterns.

Tables 3.12 to 3.14 summarize the relationships found to be relatively significant based on linear correlation coefficient. It is interesting that there appear to be more correlations between various influent parameters at the high water age location than at the entry point. However, a study of these correlations and the correlations between influent and stagnation parameters finds many of them to be confusing based on previous relationships observed in other research or to contradict each other depending on the test chamber. For example, at the entry point, lead increases with increasing aluminum concentration, while at the high water age location, lead decreases with increasing aluminum concentration. In other words, attempting to linearly correlate all these parameters is a futile effort. The relationships between the various forces at work in the distribution system cannot be defined by simple means. The effects of the forces at work in the distribution system, especially on lead and copper release and the activity of microorganisms, likewise cannot be defined by simple means. This exercise in studying possible correlations between parameters demonstrates the complexity of factors at work in a distribution system and the difficulty in drawing conclusions of causation. Data from Previous Monitoring Station and Pipe Loop Projects

Figure 3.3 shows steadily decreasing copper concentrations from the copper plate test chambers. This may imply that there is a period of adjustment or passivation that the copper plates undergo. The same adjustment period is more subtle for the lead plates in Figure 3.1.

In the previous monitoring study at NSWC that took place before, during, and after major treatment chemical changes, lead and copper concentrations are shown in Figure 3.25. Copper does appear to have a downward slope in the original water environment. That slope is interrupted by chemical changes and then the trend continues very slightly, which may or may not be significant. Lead, on the other hand, appears to be buffeted by conditions in the water environment rather than time.

Figure 3.26 shows PRS Monitoring Station data from Onalaska, Wisconsin. Copper at the entry point did not exhibit a decreasing slope over time, but the high water age copper test chamber did. Lead at the high water age test chamber is somewhat similar in shape to the copper graph.

In Figure 3.27 showing results of PRS Monitoring Station data in Waukesha, Wisconsin, high concentrations were observed during the first month of monitoring station operation just as they were in this project which may come from the installation and startup process. Otherwise, both lead and copper exhibit similar patterns and do not necessarily drop over time.

Is the PRS Monitoring Station data behavior different than data from its cousin, the Water Research Foundation pipe loop apparatus? Data from Madison and from Janesville, Wisconsin, pipe loops (Cantor et al. 2000) show copper decreasing over time for one water system but not the other. Lead data from those water systems show lead concentrations reaching somewhat of a steady state after the third week of pipe loop operation.


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In the copper mini-pipe loops of Monona, Wisconsin (Cantor et al. 2000), the copper data appeared to be greatly influenced by the concentration of polyphosphate in the water, shooting up very high at a particular dosage of polyphosphate and then slowly decreasing over time after the dosage was stopped completely.

Table 3.12 Linear correlation of influent parameters and lead and copper concentrations

Lead Concentration from 1-Pb-1:

Increases with increasing-R2= 0.5 to 0.74 none R2=0.75 to 1.0 chloride, sulfate

Decreases with increasing- R2= 0.5 to 0.74 pH, temperature, aluminum, CSMR R2=0.75 to 1.0 none

Lead Concentration from 2-Pb-1:

Increases with increasing-R2= 0.5 to 0.74 pH, aluminum, CSMR R2=0.75 to 1.0 none

Decreases with increasing- R2= 0.5 to 0.74 turbidity, iron R2=0.75 to 1.0 none

Copper Concentration from 1-Cu-1:

Increases with increasing-R2= 0.5 to 0.74 none R2=0.75 to 1.0 chloride, sulfate

Decreases with increasing- R2= 0.5 to 0.74 none R2=0.75 to 1.0 pH, temperature, aluminum, CSMR

Copper Concentration from 2-Cu-1:

Increases with increasing-R2= 0.5 to 0.74 pH, temperature, aluminum R2=0.75 to 1.0 none

Decreases with increasing- R2= 0.5 to 0.74 influent HPC_R2A, chloride, iron R2=0.75 to 1.0 turbidity


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Table 3.13 Linear correlation of influent parameters and microbiological activity

HPC_R2A Concentration from 1-Pb-1:

Increases with increasing-R2= 0.5 to 0.74 none R2=0.75 to 1.0 none

Decreases with increasing- R2= 0.5 to 0.74 none R2=0.75 to 1.0 none

HPC_R2A Concentration from 2-Pb-1:

Increases with increasing-R2= 0.5 to 0.74 none R2=0.75 to 1.0 none

Decreases with increasing- R2= 0.5 to 0.74 none R2=0.75 to 1.0 none

HPC_R2A Concentration from 1-Cu-1:

Increases with increasing-R2= 0.5 to 0.74 temperature, aluminum, drop in

stagnation monochloramine R2=0.75 to 1.0 CSMR

Decreases with increasing- R2= 0.5 to 0.74 sulfate R2=0.75 to 1.0 none

HPC_R2A Concentration from 2-Cu-1:

Increases with increasing-R2= 0.5 to 0.74 influent HPC_R2A, turbidity, iron,

chloride, sulfate R2=0.75 to 1.0 chloride

Decreases with increasing- R2= 0.5 to 0.74 CSMR, pH, temperature, aluminum R2=0.75 to 1.0 none


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Table 3.14 Linear correlation between influent parameters

At Entry Point Monitoring Station: Increases when other increases-

chloride/sulfate pH/CSMR; temperature/aluminum; total chlorine/monochloramine;

Decreases when other increases- pH/sulfate;

sulfate/CSMR At High Water Age Monitoring Station: Increases when other increases-

aluminum/CSMR; aluminum/pH; ammonia/aluminum; ammonia/CSMR; chloride/sulfate; influent HPC_R2A/iron; influent HPC_R2A/sulfate; iron/CSMR; pH/temperature; pH/CSMR; temperature/aluminum; temperature/CSMR; turbidity/total chlorine; turbidity/iron;

Decreases when other increases- aluminum/sulfate; ammonia/sulfate; influent HPC_R2A/temperature; influent HPC_R2A/aluminum; influent HPC_R2A/CSMR; influent HPC_R2A/pH; iron/aluminum; iron/CSMR; orthophosphate/CSMR; pH/iron; temperature/iron; temperature/sulfate; total chlorine/temperature; turbidity/pH; turbidity/temperature; turbidity/aluminum; turbidity/CSMR;


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Onalaska Control Chart2-Pb-1: Total Lead

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Figure 3.25 NSWC 2008/2009 monitoring during chemical addition changes

Figure 3.26 Onalaska Water Utility 2009/2010 monitoring


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Waukesha Control ChartCu2: Total Copper

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Waukesha Control ChartCu1: Total Copper

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2-Cu-1 is a high water age test chamber. Cu1 and Cu2 are duplicate test chambers. They are at the high water age station.

1-Pb-1 is an entry point lead test chamber. 2-Pb-1 is a high water age test chamber. Pb1 and Pb2 are duplicate test chambers. They are at the high water age station.

Figure 3.27 Waukesha Water Utility 2007 and 2009/2010 monitoring


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In the mini-pipe loops of Dane, Wisconsin (Cantor et al. 2003), the copper concentration decreased over eleven weeks of operation until reaching a steady state concentration while the mini-pipe loop copper concentration of Lone Rock, Wisconsin, decreased over thirty-seven weeks until a steady state was reached. The lead mini-pipe loops of both water systems appear to equilibrate quickly and then come under the influence of various forces in the water environment.

This behavior found in previous studies using PRS Monitoring Stations, pipe loops and mini-pipe loops appears to be similar to the current PRS Monitoring Station data in equilibration of lead and copper data from startup of the apparatuses. It seems that, depending on the water environment, there may be some time of passivation for copper which can be overruled if some other force in the water environment is stronger than the forces of passivation. Lead exhibits this passivation pattern to a lesser degree. It also appears that lead and copper data should not be obtained during the first three to four weeks of monitoring station operation as it may reflect effects of station startup.

Therefore, passivation of the metal plates should always be included as a possible influence on test results. Even with passivation considered, results of other forces in the water can still be observed. RESIDENTIAL DATA

Data from four residences sampled three times during the twelve months that the

monitoring stations were operating are shown in Table 3.15. Figures 3.28 to 3.31 show this data on a graph in comparison to monitoring station data taken during the same month. The graphs indicate that influent monitoring station data looks like residential influent data. That is, the monitoring station out in the distribution system is receiving water just like a residence receives water. The water environment observed at the monitoring station is within the variation of water environments found at residences in the water system.

Figure 3.32 compares the resultant lead, copper, and microbiological activity (HPC_R2A) after stagnation at the monitoring station versus the residences. Lead and copper concentrations are higher at the monitoring station than at the residences as measured in the first liter of water from the residential sampling tap. This is because the surface area of lead or copper to volume of water in the monitoring station is higher than in a house at its first-draw sampling point. However, similar magnitudes of lead levels can be seen in sequential residential sampling where water in direct contact with a lead service line is captured (Sandvig et al. 2008). The results of the monitoring station are, therefore, greater than that typically captured at residences during first-draw sampling but not unrealistic for water in contact with lead water service lines or copper water service lines and plumbing.

The microbiological activity after stagnation at the high water age monitoring station appears to be comparable to that in the residences. This activity is dependent on the microorganisms entrained in the water and flowing into the station and the residences. It is also dependent to varying degree on release of microorganisms from the metal surfaces into the water sample during the stagnation period. Because this is not totally dependent on metal surface area like the lead and copper concentrations, the HPC_R2A results fall within the same range as the residential results as was seen with influent parameters.


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Table 3.15 Residential sampling in NSWC distribution systems

Residence 1

Date PO4 Turb Cl2,

T NH2 Cl NH3 pH Temp

Fe, T Cl SO4

CS MR Cu, T

Pb, T

Log HPC

Mar 2010 0.5 0.9 1.7 1.4 7.9 44 330 14 25 0.6 37 5.5 2.7

Jul 2010 0.5 0.4 1.4 1.2 0.1 7.9 63 170 14 22 0.6 22 4.4 2.5

Oct 2010 0.5 0.7 1.1 0.9 0.1 8.0 63 280 15 24 0.6 20 6.2 3.1

Residence 2

Date PO4 Turb Cl2,

T NH2Cl NH3 pH Temp

Fe, T Cl SO4

CS MR

Cu, T

Pb, T

Log HPC

Mar 2010 0.4 0.9 1.1 1.0 8.1 63 170 15 23 0.7 61 6.2 3.1

Jul 2010 0.4 0.5 1.4 1.3 0.1 8.0 58 130 14 22 0.6 46 5.4 2.5

Oct 2010 0.5 0.7 1.2 1.1 0.2 7.9 62 180 14 23 0.6 49 9.1 2.7

Residence 3

Date PO4 Turb Cl2,

T NH2Cl NH3 pH Temp

Fe, T Cl SO4

CS MR

Cu, T

Pb, T

Log HPC

Mar 2010 0.5 1.5 1.3 1.1 0.2 7.9 60 270 15 23 0.7 9.3 5.6 2.7

Jul 2010 0.5 0.4 1.4 1.2 0.0 8.0 63 170 14 22 0.6 9.3 5.5 3.1

Oct 2010 0.6 0.3 1.3 1.2 0.2 7.9 63 110 14 23 0.6 9.0 4.9 2.5

Residence 4

Date PO4 Turb Cl2,

T NH2Cl NH3 pH Temp

Fe, T Cl SO4

CS MR

Cu, T

Pb, T

Log HPC

Mar 2010 0.5 0.7 1.7 1.5 0.1 7.9 47 100 14 24 0.6 190 3.0 1.8

Jul 2010 0.4 0.2 1.6 1.4 0.0 8.0 59 43 14 22 0.6 79 3.0 2.0

Oct 2010 0.6 0.2 1.6 1.2 7.9 64 32 14 22 0.6 73 5.0 2.7

Notes: PO4=orthophosphate in mg/L as PO4 Cl=chloride in mg/L Turb=turbidity in NTUs SO4=sulfate in mg/L Cl2,T=total chlorine in mg/L CSMR=chloride to sulfate mass ratio NH2CL=monochloramine in mg/L Cu,T=total copper in µg/L NH3=free ammonia in mg/L Pb,T=total lead in µg/L pH=pH in Standard Units Log HPC=base 10 log of heterotrophic plate Temp=temperature in degrees F count in colony-forming units/mL Fe,T=total iron in mg/L


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Figure 3.28 Residential sampling in NSWC distribution systems: orthophosphate, turbidity, total iron


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Figure 3.29 Residential sampling in NSWC distribution systems: total chlorine, monochloramine, free ammonia


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Figure 3.30 Residential sampling in NSWC distribution systems: pH and temperature


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Figure 3.31 Residential sampling in NSWC distribution systems: chloride and sulfate


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Figure 3.32 Residential sampling in NSWC distribution systems: lead, copper, HPC


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LEAD AND COPPER RULE DATA Data collected during Lead and Copper Rule compliance sampling consists of total lead

concentration and total copper concentration in water that has stagnated in residences. The regulation puts emphasis on the ninetieth percentile concentration of the sampling pool. Other statistics that are useful for comparison in these datasets are average concentration of the sampling pool and the maximum concentration found in the sampling pool. Figure 3.33 shows these statistics for lead and copper in the NSWC distribution systems.

Recall that disinfection in the NSWC system was changed to chloramine from free chlorine in November 2008. The corrosion control chemical was changed just before the disinfection switch – from a 50/50 polyphosphate/orthophosphate blend product to a 10/90 product (mostly orthophosphate). Offline chemical comparison tests and distribution system monitoring with the PRS Monitoring Stations both showed that the average lead and copper concentrations dropped after the chemical changes. In addition, the variation of these concentrations greatly narrowed, where copper concentration improvements were less dramatic than the improvements for lead concentration.

Data from the Lead and Copper Rule sampling show that the average copper concentration in the compliance sampling dataset stayed about the same as seen historically in 2009 after the chemical changes. However, the ninetieth percentile concentration and the maximum concentration were lower by about 60 µg/L or about 24% of 2006 sampling results.

With lead concentration, average, ninetieth percentile, and maximum concentration all were found to be lower in 2009 than found previously. The ninetieth percentile was no longer hovering just below 15 µg/L, the borderline of compliance. The maximum concentration dropped by 8 µg/L or about 28% of 2006 sampling results.

METAL PLATE MICROBIOLOGICAL ANALYSIS

The results of microbiological tests performed on the suspension of biofilms removed from the metal plates are shown in Table 3.16. Copper plates had greatly higher heterotrophic bacteria counts but lower iron bacteria counts than lead plates. Heterotrophic bacteria populations were higher at the high water age location in the distribution system than at the entry point while iron bacteria populations were about the same at both locations. No sulfate-reducing bacteria were detected on any plates.

Scanning electron microscopy showed extracellular polymeric substances indicative of biofilms on both lead and copper plates. The copper plates, however, showed the presence of radiating ribbon-like stalk structures suggesting the presence of a filamentous or stalk forming bacterium. The extracellular polymeric substance on the copper plates was more intermittent than that on the lead plates where the coverage is more continuous. Details of the metal plate microbiological analyses are described in Appendix B and C.


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Figure 3.33 NSWC Lead and Copper Rule sampling results


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Table 3.16 Microbiological tests performed on PRS Monitoring Station metal plates

Sample HPC_R2A Iron Bacteria Sulfate-Reducing Bacteria CFU/in.2 Cells/in.2 Presence/Absence

1-Cu-1 7700 13 absent 2-Cu-1 44000 16 absent 1-Pb-1 11 410 absent 2-Pb-1 320 359 absent

METAL PLATE CHEMICAL ANALYSIS

Visually, the lead plates could be seen to have a relatively even coating of white scale,

with some additional scale types developed under the spacer in the center of the plate. The copper plates by contrast have thinner, patchy scale development.

Compounds found on the metal plates are summarized in Table 3.17.

Table 3.17 Compounds found on PRS Monitoring Station metal plates

Chemical Compounds

Lead Plates Copper Plates

Carbonate Hydrocerussite [Pb3(CO3)2(OH)2] minor amts. of Azurite [Cu3(CO3)2(OH)2] minor amts. of Cerussite [PbCO3] Calcium Calcite [CaCO3] Calcite [CaCO3] Aluminum Aluminum hydroxide [Al(OH)3] insignificant Sulfate none none Iron present insignificant Phosphorus Pyromorphite [Pb5(PO4)3OH] minor amts. of Apatite [Ca10(PO4)6(OH)2] Oxide Litharge [PbO] Cuprite [Cu2O]

Carbonates are present on both the lead and copper plates. On the lead plates,

hydrocerussite is present as well as a minor amount of cerussite, a less soluble and more protective compound. The lead plates from the high water age monitoring station (2-Pb-1) had substantially greater scale development than the lead plates from the distribution system entry point (1-Pb-1), although both have the same minerals and proportions of those minerals. Copper carbonates on the copper plates were detected but not in significant amounts.

Calcium in the form of calcite was present on both lead and copper plates with the same amounts at both monitoring stations.

There was a considerable quantity of aluminum found on the lead plates, but not on the copper. This phenomenon may be based on the isoelectric points (IEP) for the minerals involved. The isoelectric point is the pH at which a particular surface carries no net electrical charge. The net charge is affected by the loss or gain of protons (H+) and is, therefore, a function of pH in the adjacent water.

The IEP for lead carbonate is 6.2 so the surface charge is negative above pH 6.2. Aluminum hydroxide has an IEP of 9.1 and, therefore, is positively charged below pH 9.1. These positively-charged small particulates of aluminum entrained in the water are attracted to the negatively charge lead carbonate film on the lead plate surfaces and can form deposits on the


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lead plates. The copper plates, on the other hand, have intermittent films of cuprite with an IEP of 10.8 (that is, positively charged below pH 10.8). The positively-charged aluminum will not be attracted to the positively-charged copper plates.

Higher amounts of aluminum were found on the 2-Pb-1 plates than the 1-Pb-1 plates. This indicates that aluminum is more in a dissolved form at the entry point. At the high water age location in the distribution system, the aluminum is found as small positively-charged entrained particulate matter.

A previous study in Toronto also found high aluminum scale associated with lead components and increased dissolved lead in the water (Sandvig et al. 2008). In that study, dissolved lead in the water appeared to decrease and particulate lead increase as temperature and pH increased. This would be indicative of solubility characteristics of lead carbonate on the lead surfaces. But, particulate lead releases were found to increase with increasing temperature and pH. This could be explained by the dissolution of aluminum hydroxide which becomes more soluble at higher temperature and pH with possible disruption of adjacent lead carbonate scales. This phenomenon requires more study. Nevertheless, a similar interplay between aluminum and lead may be occurring in the NSWC system.

No sulfates were found on either lead or copper plates. Sulfates are typically found forming copper sulfates on brass but not on pure copper components; sulfates are rarely found on lead components. Sulfates are also typically seen in iron scales, such as those on unlined cast iron piping where sulfate reducing bacteria are also typically found.

Iron was found on lead plates but not significantly on copper plates. The iron might follow a similar process as the aluminum.

A coating of phosphorus is found on both the lead and copper plates. Higher amounts of the phosphorus compound, pyromorphite, are found on 2-Pb-1 plates than on 1-Pb-1 plates. About the same amounts of phosphorus were found on both the 2-Cu-1 plates and the 1-Cu-1 plates but at very low levels. The intent of adding orthophosphate to the water is to form these relatively insoluble phosphorus films on metal surfaces to prevent uniform corrosion from occurring. This shows the formation of the films is occurring, mostly on lead surfaces.

The oxide, litharge, is found on lead plates and the oxide, cuprite, on copper plates. No highly oxidized phases such as plattnerite (PbO2) or tenorite (CuO) were found in the x-ray patterns, and so those compounds, if present, comprise less than 10% of the scale. The compounds in the higher oxidation state are less soluble and, therefore, more protective against transfer of lead and copper into the water than the compounds of lower oxidation state that have been observed on the plates. In terms of the existing cuprite on the copper plates, those scales tend to form slowly. SCADA DATA

SCADA data provided more data to study in context with the PRS Monitoring Station

data. Also, the use of Shewhart control charts on SCADA data demonstrates the usefulness of this tool on any measurement taken over time, whether it is chemical or physical.

Shewhart control charts were created for:

Gallons water entering the distribution systems Hourly change in treatment plant clearwell depth Chemical feed in pounds of chemical per 1000 gallons of treated water


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o Alum o Polymer o Fluoride o Orthophosphate product o Chlorine o Ammonia

Figures 3.34 to 3.36 show the data patterns that form on the Shewhart control charts for

this SCADA data. For the physical data plotted in Figure 3.34, the Shewhart control charts do not necessarily make sense in terms of a means for control of flow or clearwell depth change. However, the Shewhart control chart statistics plotted on the charts simply serve as a visual guide. The seasonal trend of the data for filtered water flow becomes obvious when the data are plotted over time, especially with the enhancement of guidelines layered over the data. So, in this case, the Shewhart control chart is not being used for process control or improvement; it is being used to better visualize the data patterns.

The chart of the clearwell depth change shows some interesting data points when the depth change data point jumps outside the boundary of expected variation. For example, a depth change of -4.1 feet on 7/8/2010 reflects an issue with the raw water pumps. Production was shut down until the problem could be resolved.

With the chemical addition data in Figures 3.35 and 3.36, each chart shows excursions of the parameter outside of the typical variation. Some of these excursions are brief with data falling back into the control limit range on the next data point. Those high data points should be checked to see if there is an issue with the measurement method or if these fluctuations have a physical reality. Can these excursions be prevented? Is there system control or financial benefit in controlling them?

Alum addition experienced high variability from November 2009 to March 2010. This reflects the highly variable raw water turbidity entering the plant in that time period. Polymer dosage was low in April and May. The orthophosphate graph reflects the test in lowering the dosage between May and July.

ON-LINE SENSOR DATA

On-line water quality parameter sensor data are also sent to the SCADA system. The

Shewhart control charts can make sense out of that data as well. The turbidity of the water flowing out of the filter and the turbidity of the water entering the distribution system are both captured with on-line sensors at NSWC with the information relayed to the SCADA system.

Figure 3.37 demonstrates the use of Shewhart control charts with this data. There appears to be a seasonal pattern to the turbidity of the filtered water. That pattern is similar to Figure 3.34 showing gallons of filtered water produced. Perhaps, when the filters are being used at a greater percentage of their capacity, the filtered water turbidity increases slightly.

The spikes in turbidity of the filtered water reflect maintenance performed on the on-line turbidity instrument.


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Figure 3.34 Shewhart control charts used for physical data


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Figure 3.35 Shewhart control charts used for other chemical feed data


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Figure 3.36 Shewhart control charts used for disinfection chemical feed data


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Figure 3.37 Shewhart control charts used for on-line sensor data


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The entry point water turbidity follows the same pattern as the filtered water turbidity except for an episode in January and February 2010. At that time, the phosphate injection point at the treatment plant was found to produce a precipitate; the injection point was changed. The precipitate and the work done on the equipment probably increased the entry point water turbidity.

Other variations in the turbidity in spring and summer 2010 may result from other work being performed on equipment in the water treatment plant. At that time, new SCADA system hardware and software were being installed and tested, disturbing normal operation of the plant.

REGULATORY AND INVESTIGATIVE DATA

The usefulness of Shewhart control charts can also be seen with any routine regulatory

data. Figures 3.38 and 3.39 show the NSWC Total Coliform Rule sampling site disinfection data on control charts for each sampling site.

The NSWC has taken advantage of the sampling sites that must be visited by regulation by measuring turbidity at each of those sites. Figures 3.40 and 3.41 show the turbidity data on control charts for each sampling site.

From the disinfection charts, a quick look makes it evident that Site 6 has a lower average disinfection concentration than the other sites. Sites 4 and 5 have the highest average concentrations. Some sites have a narrow variation of concentrations like Site 15; some sites have a wide variation like Site 16. There are times when sites have temporary atypical concentrations that cross the control limit lines; Site 16 experiences wild behavior in crossing the line and large fluctuations during the summer in 2010. There is a common pattern to the disinfection concentration that may be dependent on season or temperature. That is, disinfection concentration drops starting in April and May and climbs back up beginning in August and September.

For turbidity, some sites routinely stay at a very low turbidity level, such as Sites 5, 13, 15, and 16. For the sites that display higher turbidity, a pattern appears where turbidity drops through the early part of the year to reach a low point in August and then begins an increase. The high turbidity in early 2010 was higher than the high turbidity in early 2011. Sites 1, 2, 3, 6, 9, and 10 exhibit this pattern. Site 14 experiences lower turbidities from September to December 2010.


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Figure 3.38 Shewhart control charts used for regulatory distribution system disinfection data: Part I


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Figure 3.39 Shewhart control charts used for regulatory distribution system disinfection data: Part II


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Figure 3.40 Shewhart control charts used for extra distribution system data taken during Total Coliform Rule compliance sampling: Part I


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Figure 3.41 Shewhart control charts used for extra distribution system data taken during Total Coliform Rule compliance sampling: Part II


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CHAPTER 4 DISCUSSION AND CONCLUSIONS

This study demonstrated the basic structure of a comprehensive consumer-centric process control methodology. Trends in water quality at consumers’ taps were characterized. The information was compared to trends in the same parameters at the distribution system’s entry point. The information was also compared to physical and chemical aspects of the water treatment process and influent source water. In this way, effects on the consumers’ water quality could be observed as seasonal, unplanned, or intentional changes occurred anywhere in the water system.

This is a methodology that:

1. Is comprehensive: Identifies specific information that links the water sources, treatment, distribution system, and delivered water quality together.

2. Is consumer-centric: Uses product quality – that is, delivered water quality – as the main focus of process control endeavors, allowing it to drive decision-making, expenditures, and planning for the complete water system.

3. Enhances process control: Is proactive in water quality control and improvement rather than allowing regulatory compliance and customer complaints to motivate such efforts.

Figure 4.1 is a diagram of how all these elements fit together.

Figure 4.1 Tools and concepts introduced in this study


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Figure 4.2 A comprehensive consumer-centric process control method for drinking water systems


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REPRESENTATIVE DISTRIBUTION SYSTEM WATER SAMPLES

In achieving this desired consumer-centric process control for water systems, obtaining water samples from the distribution system that are representative of the water quality that the consumer drinks is key. Did this project successfully capture the water quality that the consumer drinks?

Water distribution systems are very complex with greatly varying piping configurations, water flows, chemical reactions, and microbiological activity (Cantor 2009). The strategy used in this project was to study two extreme environments in the distribution system. One extreme is the location in the distribution system where the freshly treated water enters. At this point, the water has been recently pulled from the source, water treatment chemicals have been recently introduced, and there has been little interaction with piping systems. The opposite extreme is a location in the distribution system where water has been in the system for a long period of time, that is, a location of high water age. At this point, the water has had a lot of time to interact with piping materials, chemicals, and microorganisms.

A water system may have more than two such critical points. Part of the strategy of monitoring is to determine the locations of such critical and extreme points in a water system and prioritize them (Cantor 2009). The budget may not allow for monitoring at all the critical locations. However, monitoring at least one high water age location in a distribution system will yield more insight on consumers’ water than not monitoring at all.

In this project, there are three distribution systems connected to the NSWC water treatment plant. One monitoring station was located at the entry point in Glendale. The second monitoring station was located at a location of relatively high water age, also in Glendale. The data from the monitoring stations were compared to data from three residences in Whitefish Bay that, in past Lead and Copper Rule sampling, had elevated lead levels. Figures 3.28 to 3.31 compare the influent water quality parameter concentrations between the monitoring stations and the residences, where the y-axis scale greatly magnifies very small concentration changes. The monitoring station data of orthophosphate, turbidity, total iron, total chlorine, monochloramine, free ammonia, pH, temperature, chloride, and sulfate stay within the same range and generally follow the same magnitude and trends as the residential data. The monitoring station appears to be just another “residence” connected to the water distribution system.

The influent water quality parameters to the monitoring stations and the residences characterize the “fresh” flowing water at those specific locations in the distribution system. What happens when this water reacts with the piping material at those locations? In this project, reactions that produce changes in lead, copper, microbiological activity, monochloramine, ammonia, nitrite, and nitrate were studied. Many other reactions in water could be studied for the benefit of understanding consumer water quality.

In terms of reaction time, keep in mind that the consumer does not always drink “fresh” flowing water. The consumer drinks water that has sat for varying lengths of time in the premise plumbing where chemical and microbiological reactions can occur over the various time periods. If the water quality is to be characterized for the consumer, a water stagnation time period is necessary to allow these reactions to occur. This concept has been promoted in the Lead and Copper Rule with the intent of the rule to observe the most extreme concentration of lead and copper in consumers’ water (CFR 40:141:I).

If the results of the reaction time are to be compared from sampling event to sampling event and from location to location, proper experimental design needs to be instituted. That is,


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factors extraneous to the actual reactions need to be held constant. Otherwise, the extraneous factors could influence the results rather than the results solely being a product of the chemical and microbiological reactions alone and conclusions from the data could be flawed. Some extraneous factors are length of time for water stagnation, piping configuration, materials of construction and water flow. Obviously, these factors cannot be controlled in a residence; they are controlled in the monitoring stations, making monitoring stations a more scientific approach to understanding trends in consumer water quality.

At this point in the discussion, it is important to state that the monitoring approach demonstrated in this project is for process control where a strict comparison of water quality trends is critical. Evaluation of consumer water quality for health risk assessment has different requirements (Cartier et al. 2011). Risk assessment sampling has more complex questions to answer and typically cannot be performed routinely. Monitoring for process control can and must be performed routinely. Both types of monitoring are important, with the data from one type informing the other type. This project emphasizes that water systems need process control in place before risks from drinking water contamination can be controlled.

Returning to the concept of process control, comparison of reactions in the monitoring station to those at residences are shown in Figure 3.32. Some reactions depend directly on piping configuration – on surface area of piping material exposed to a volume of water. Figure 3.32 shows that lead concentration ranged from 60 to 100 µg/L at the lead test chamber in the high water age monitoring station while residential first-draw samples at the kitchen faucets were below 10 µg/L. This large difference in lead concentration makes sense in the light that the lead-surface-area-to-water-volume exposure used in the monitoring station is greater than would be found in a first-draw kitchen tap sample at a residence. However, when water samples are drawn in succession at a residence to create a “lead profile” of a residence with a lead water service line, concentrations of lead greater than 60 µg/L and 100 µg/L are common where a water sample corresponds to a location within a lead water service line (Sandvig et al. 2008). That is, in a lead service line, the lead-surface-area-to-water-volume exposure is similar to that in a PRS Monitoring Station or any other monitoring device based on a Water Research Foundation pipe loop apparatus.

Likewise, copper concentrations from the copper test chamber of the high water age monitoring station in Figure 3.32 are similar to concentrations that would be seen from a copper water service line or copper plumbing in a residence. Those concentrations are higher than copper concentrations found in the first-draw stagnation samples at the residences where the volume of water captured in the sample is exposed to some non-copper materials as well as copper. But, unlike the lead concentrations, the concentration of copper at the monitoring station is somewhat close to those found in the residences. This is because there are more copper components than lead-based components in residences in the pipe section that the first-draw water sample contacts. Figure 3.32 confirms that the monitoring station copper concentration is closer to that found in residential first-draw samples than the monitoring station lead concentration.

In summary, the PRS Monitoring Station is designed with the metal-surface-area-to-exposed water-volume ratio of a 1.5 inch diameter pipe. (The ratio was based on physical constraints of the monitoring station.) This surface area produces lead and copper concentrations that are similar to lead and copper piping in residences but greater than the concentrations found in residential first-draw samples where mixed materials are exposed to the water captured in the sample.


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Stagnation period interaction with microorganisms at monitoring stations versus residences is also shown in Figure 3.32. In this case, the measurement of microbiological activity (using a sensitive method for heterotrophic plate count) is that of microorganisms entrained in the water. Some come from the microorganisms already in the influent water that are carried into the test chambers or residences. Other microorganisms are those that are released from biofilms on metal plate surfaces and pipe walls into the water. So, only a component of the total stagnation HPC_R2A is dependent on piping surface area. In addition, every surface has a chance of having biofilms attached, so the surface-area-to-water-volume exposure for biofilms would be more similar between monitoring stations and residences than would lead or copper surface-area-to-water-volume exposure for first-draw residential samples. Figure 3.32 confirms that the resultant measurements of microbiological activity from the monitoring station test chambers fall within the same range as that from the residences.

The benefit of knowing water quality data trends can be seen in comparing the NSWC monitoring station data from the previous monitoring efforts (Figure 3.25) to Lead and Copper Rule sampling data (Figure 3.33). Lead and Copper Rule sampling takes a snapshot of the state of water quality in terms of lead and copper every three years for a water system already in compliance with the regulation. The monitoring station takes repeated snapshots as often as desired. During the major change in treatment chemicals, NSWC management was able to track the changes in lead and copper concentration daily and weekly. If lead had been seen to increase dramatically at any point, management could have warned the public and changed course. If only Lead and Copper Rule sampling was available and lead had increased, the problem would not have been discovered until after considerable consumer exposure. Fortunately, the change in treatment chemicals showed an improvement in water quality trends – the lowering of average lead concentration in the water and the narrowing of lead concentration variation. The “snapshot” taken by the Lead and Copper Rule sampling in 2009, eight months after the treatment chemical change, confirmed a lower average lead concentration, lower ninetieth percentile lead concentration, and narrower range of observed lead concentrations as was measured three years before that time. Similar findings and comparisons were shown for copper concentrations.

With the above discussion on the similarities of influent water quality parameters and stagnation reactions, it can be concluded that the monitoring stations do represent consumers’ water quality. The monitoring stations have the added benefit of gathering data under scientifically similar conditions and providing accessibility for routine sample gathering. The trends in the data from the monitoring stations can be used as the basis for consumer-centric process control.

With that praise for monitoring stations comes an admonition. Delivered water quality is not just dependent on interactions with the piping material itself. Chemical scales and biofilms build up on the surface of piping materials and interact with water. The scales and biofilms are the product of interactions of the piping material with water and the influencing environmental factors in the piping system. A valid argument against monitoring stations or pipe loops is that the metal surfaces used in these experimental apparatuses are new and have not developed the chemical scales and biofilms that define water quality from existing old pipes in the distribution system. Nevertheless, we have already seen that monitoring station results were very similar to residential results in defining water quality trends.

At the same time, it is acknowledged that chemical scales and biofilms are important factors in the dynamics of delivered water quality. More than that, the compounds that are found


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on the piping surfaces and even those that are not found on the piping surfaces give insight into the key chemicals and water environment criteria that define the water quality for a water system. Analysis of these scales and films are essential to understanding the nature of a water system.

A suitable monitoring station, once again, has an advantage over other means of exploring the nature of the distribution system. For example, the PRS Monitoring Station test chambers are filled with stacks of small metal plates. These plates are easily removed after a time period of exposure to water and sent for chemical and microbiological analysis. No pipes need to be dug up from beneath the ground; no plumbing systems need to be torn apart for these analyses.

In addition, the PRS Monitoring Station has an advantage over a typical pipe loop apparatus in that the metal plates require little preparation for analyses. Pipes from pipe loops, on the other hand, require longitudinal cutting which can interfere with the internal scales to be studied. The sliced pipes also present a curved surface to the analytical instruments which can make analysis more difficult, while the metal plates from the PRS Monitoring Station presents a flat surface for analysis.

For NSWC, the analyses of the metal plates show that biofilms develop on both lead and copper materials. In this water system, iron bacteria are present on lead materials while heterotrophic bacteria thrive on copper materials. Sulfate-reducing bacteria are not players at all on those materials because sulfur compounds do not form on them even though there is sulfate dissolved in the water.

The NSWC plates do not have especially protective inert chemical scales naturally. On lead, hydrocerussite is more prevalent than cerussite, a less soluble, and therefore, more protective chemical scale (AWWA and DVGW-TZW 1996). Litharge is predominant rather than its less soluble, more protective cousin, platternite (Lytle and Schock 2005). Calcite, which is present, affords no special protection against corrosion (AWWA and DVGW-TZW 1996). However, the phosphate added for the sake of lowering lead concentrations is forming a film of the protective orthophosphate and lead compound, pyromorphite. But, there is a spoiler that lurks in the water, and that is aluminum hydroxide. The aluminum is added to the water back at the water treatment plant to aid in filtration. It is carried out into the distribution system where it precipitates as aluminum hydroxide onto lead materials and other materials that are negatively charged. The aluminum hydroxide scale then proceeds to build-up or dissolve as factors in the water environment change. With its transformations, aluminum can possibly cause disruption to adjacent lead carbonate scales or even sorb lead and carry it into the water as entrained particles. In these ways, aluminum has become a major player in NSWC delivered water quality.

Orthophosphate compounds are slower to form on copper surfaces in NSWC. (The orthophosphate dosage is relatively low.) In this case, orthophosphate is probably not providing as much protection as orthophosphate films on the lead plates. Cuprite is forming slowly and gives some protection, although not as much protection as its less soluble cousin, tenorite. Aluminum does not play a role in copper’s release to the water.

The combination of the flowing water analyses, the stagnating water analyses, and the metal plate analyses – all from monitoring stations at critical locations – gives insights into the consumers’ delivered water quality.


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SHEWHART CONTROL CHARTS

Data from the monitoring stations were evaluated with statistics calculated for Shewhart control charts. From the charts, it is seen how the data points move above and below their average value. The Shewhart control charts are a visual aid to determine the data trends.

In addition, the Shewhart control charts apply a special calculation of data variance. They show the limits of that variance as guidelines to data that can be expected in contrast with data that have failed those expectations and are atypical. On a Shewhart control chart, no data point is considered an “outlier” to be tossed away. Any data point outside of the boundaries of expected variation or breaking with other expected behavior should inspire managers to ask themselves what changes in the water system might have caused the atypical behavior.

Shewhart control charts were also applied in this project to compare data from location to location in the water system, from time period to time period in the water system, and even to compare data between water systems. The variance of the data as calculated using Dr. Shewhart’s concept conservatively shows whether or not datasets fall into the same range of results or whether they are significantly in different result ranges.

In addition, operational tests can be run in an effort to move the average to a desired value and to narrow the distance between the boundary lines, thereby narrowing the variation of the parameter. In the previous NSWC project, the Shewhart control charts showed that after two major chemical changes – corrosion control chemical and disinfection chemical – the process was improved in that the average lead concentration from the monitoring stations’ test chambers decreased and the variation of lead concentration narrowed. This is an example of documented process improvement.

There are other aspects to using Shewhart control charts that are covered elsewhere (Wheeler and Chambers 1992). For example, the boundary lines (control limits) can be specified and compared to data on a chart to impose an ideal range. The reader is encouraged to explore these other possibilities for quality control and process improvement.

In addition to monitoring station data, the Shewhart control charts can be applied to any time series data. This was demonstrated with physical operational data from the SCADA system (Figures 3.34 to 3.36) and chemical data sent by on-line sensors to the SCADA system (Figure 3.37). With these charts, seasonal trends are documented and put in perspective; operational issues, such as the raw water intake pump failure in Figure 3.34, are documented and their significance to the water system determined. These charts are visual diaries of water system operation.

Shewhart control charts used for time series regulatory data were demonstrated with regulatory sampling data (Figures 3.38 to 3.41). In this example, NSWC records turbidity measurements during their routine Total Coliform Rule data collection of total coliform and disinfection concentration water samples. On February 8, 2011 the total coliform test came back positive at Whitefish Bay Site 3. The sampling point is in a school, which, at the time, was undergoing construction. NSWC personnel saw that the turbidity measurement on the sampling day was at 1.12 NTU, close to the upper boundary of typical variation. With the positive total coliform result, the turbidity took on new significance. NSWC personnel asked what was going on in the school’s plumbing system on the day of sampling. It turns out that the building contractor was flushing water through the plumbing system in order to flush out the plumbing in the new construction. Contaminants were either stirred up in the water system or were sucked into the system with the flushing. NSWC personnel returned to take a check sample for total


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coliform. Now, the turbidity was lower, there were no further positive total coliform samples. The Shewhart control charts of turbidity and chlorine tell a story at each Total Coliform Rule sampling site, which can represent operational or localized issues and can sometimes represent a direct threat to public health. Overall, the charts can portray data at locations in the distribution system that tend to have lower disinfection concentrations or higher turbidity levels. To improve the distribution system water quality, that is, raise disinfection to a desired level and lower turbidity, one can search for reasons why specific locations show the results and trends that they do and make appropriate changes to the water system or to water system operation.

In general, the Shewhart control charts’ average and lines of expected variation provide visual guidelines to quickly put data into perspective and trigger awareness of the possibility that something is different in the water system today than it has been historically. They can, also, serve as a comparison tool to point out underperforming areas of operation. And, they can be used to strive for a desired average and narrowed variation of results, thereby providing a means of process improvement.

In these ways, the Shewhart control charts can be used as an important tool for any of the operational improvement techniques and programs, such as the AWWA Utility Quality Program (AWWA 2011), the Effective Utility Management group (APWA et al. 2008), and The Partnership for Safe Water (PSW 2011). Each of these programs encourages the use of comparison of utility data to operational benchmarks from accumulated utility experience. The Shewhart control charts allow an individual water system to create their own benchmarks specific to the nuances of the individual water system in addition to using the benchmarks of others.

EXAMPLE OF AN ITERATIVE PROCESS CONTROL METHODOLOGY

As can be seen in this project, a lot of water system data can be produced while trying to observe as many factors as possible. We want to interpret the data and look for the influences that make the water quality better and those that make it worse. However, one must be careful about drawing conclusions of causality in correlating data as was illustrated in the effort to correlate parameters in this project. All that can be done is to develop hypotheses based on past observations. Then, test those hypotheses with the on-going monitoring of the water system. Finally, determine if the new data prove the new hypotheses, suggest different hypotheses, or confirm old hypotheses. In this way, process control is an iterative method as shown in Figure 4.2.

This project provided an excellent example of the iterative method of process control and improvement. The intentional lowering of orthophosphate dosage appeared to increase the lead concentration at the high water age monitoring station. If only orthophosphate dosage and lead release at the high water age monitoring station had been collected, it might have been concluded that the orthophosphate dosage directly effected lead transfer to water in the NSWC water system. Because other information was gathered during that critical period, there are doubts that there was such a direct causality. The reasons for the doubts are as follows:

It would not seem that such a small change in orthophosphate dosage could

create such a large response in lead release. The concentration of orthophosphate was cautiously lowered 0.07 mg/L; the lead concentration at the high water age monitoring station increased quickly from about 60 µg/L to about


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110 µg/L. When the orthophosphate was returned to the original dosage several weeks later, the lead concentration at the high water age station decreased slowly over several more weeks back to 60 µg/L.

If orthophosphate had a direct effect on lead, it should have been seen at the entry point as well as the high water age station. The lead at the entry point monitoring station stayed at around 60 µg/L before, during, and after the orthophosphate dosage change. (See Figures 3.1, 3.2, 3.13, and 3.14 for graphs of lead concentration and orthophosphate dosages.)

The pattern of filtered water production looks as if it could be a factor in increased lead release. Looking for other factors that may have influenced the lead at the high water age location, it is seen from Figure 3.34 that at that same time period the orthophosphate dosage was lowered, the filtered water production increased for higher summer demand.

An odd pattern in iron particles and turbidity occurred just before and during the orthophosphate dosage adjustment which could be a factor in lead release or could be another effect of some other factor that also affected the lead concentration. At the high water age monitoring station, turbidity followed a very strange pattern of a steep decrease starting in January and continuing until the end of August when it began to rise again. The strange decrease is confirmed in the chart of total iron (Figure 3.19). Iron is typically observed in particulate form out in the distribution system as iron pipeline corrosion scales crumble into the water and make up part of the turbidity measurement. The dissolved iron concentration stays low, so the charts show that high water age location particulate iron is decreasing over time, including the time period that the orthophosphate has been lowered.

Something during this time period increased the solubility of lead and copper but not iron out in the distribution system and the crumbling of iron scales decreased. Looking back at the lead concentration in Figure 3.1, it is seen that dissolved lead, not particulate lead, is increasing during the time period of concern. Copper (Figure 3.3) at the entry point monitoring station does not veer from its course during the lowering of orthophosphate, but at the high water age monitoring station, the copper, which is mostly in dissolved form, hesitates in its downward trend during the time period.

In summary, the metal transfer phenomena, in this case, may or may not be influenced by

orthophosphate addition. Whatever the factors are, it does not occur when the water is fresh from the treatment plant. And, the phenomena may have something to do with summertime water demand and warmer water temperatures.

It is disappointing that the monitoring stations did not give a clear answer to a cause and effect question. We have to accept that as the nature of an empirical approach. But, what do we do now? What kind of decisions can be made? What we do is make hypotheses about cause and effect and use on-going monitoring to prove or disprove our hypotheses.

First of all, we have learned that there are interesting patterns that occurred in the high water demand, hot months. A good strategy is to look for this seasonal trend again in the future to see if it can be confirmed.


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Secondly, we would want to try the same cautious experiment of lowering the orthophosphate dosage outside of that critical time period, at a time of cooler temperature and lower water demand.

Finally, there may be other factors that are causing the observed trends. We need to be on the lookout for such factors and incorporate them into our measurements. For example, we learned from the metal plate analysis that aluminum compounds build up on the lead surfaces. Perhaps we should focus on the aluminum trends and study when it is dissolved and when it is in particulate form. The total aluminum concentration steadily increases as the temperature warms starting in March (Figure 3.15). When the temperature begins to cool in the autumn, we get greater variation in the aluminum concentration but it looks as if it might be returning to the lower concentrations seen a year earlier in November and December. In Figure 3.35, a large variation of alum dosage occurs in the autumn and winter; a lower steadier dosage of alum begins in March and continues for the rest of the project. Since the added aluminum is steady, the increase of aluminum concentration measured in the distribution system must come from the breaking up or dissolution of aluminum scale on pipe walls. If aluminum scale is being transferred into the water, it would make sense that lead, intermingled with aluminum scale, would transfer also. It is not known if this is the actual cause of the increased lead, but there is enough evidence that it is not orthophosphate dosage alone controlling the transfer of lead, copper, and iron; higher water usage, pH, temperature, and the stability of aluminum scale are as equally plausible as the cause of the observed metal transfer. Closer observation of particulate and dissolved aluminum trends is warranted for on-going monitoring. SUMMARY OF A COMPREHENSIVE CONSUMER-CENTRIC PROCESS CONTROL METHODOLOGY

In this project, the argument was made for monitoring the quality of water delivered to the consumer and using that information to control water system operations for the distribution system, the water treatment facilities, and the water source. This project began with characterizing delivered water quality and followed back through the treatment plant by defining operational trends on control charts. This section ties the information together in a comprehensive perspective of process control.

Distribution System Process Control: Delivered water quality versus entry point water quality

Monitoring the quality of water delivered to the consumer has been difficult in the past. However, this project used PRS Monitoring Stations as an abstraction of consumers’ plumbing systems to chronicle the environment in the distribution system and the trends of the resulting reactions from that environment.

The data showed similarities and differences between the high water age location (the worst case delivered water quality) and the entry point to the distribution systems (the freshest water). Differences found were:

Lead transferred to water is greater at locations of high water age than at the entry

point. And, there are other forces in the distribution system, which also influence the lead transfer trends that are not significant at the entry point. A major


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example in this project was an increase in lead seen at the high water age station in the summer, but not seen at the entry point.

Copper transferred to water is the same or greater at locations of high water age than at the entry point. And, there are other forces in the distribution system, which also influence the copper transfer trends that are not significant at the entry point. A major example in this project was a hesitation in dropping copper levels seen at the high water age station in the summer, but not seen at the entry point.

Microbiological activity follows different trends at the high water age location versus entry point and is dependent on the type of metal.

Assimilable organic carbon is slightly increased at the high water age location. Temperature is slightly higher in the warmer months at the high water age

location. Disinfection is about 25% lower at the high water age locations. Free ammonia has formed at the high water age locations from some dissociation

of chloramine compounds; it is not at significant levels at the entry point. Iron particulates are present at the high water age locations to a larger degree and

exhibits different trends than at the entry point. Turbidity follows similar trends as iron particulates.

Similarities found were:

Alkalinity, hardness, and total dissolved solids were similar at both locations. Monochloramine concentration decreases and free ammonia increases slightly

during the stagnation of water at both locations. Nitrification does not appear to be occurring at both locations. The pH, orthophosphate, aluminum, sulfate, and chloride are similar at both

locations. It can be concluded that the distribution system has an environment that is more

conducive to microbiological activity (lower disinfection, higher concentrations of nutrients such as carbon and nitrogen, higher temperature) and includes more scales and films (iron particulates, turbidity) than at the entry point. There is also a longer time available for chemical reactions to occur. Operational changes in the distribution system should be based on efforts to shorten the water age, clean out pipe scales and films, and use increased disinfection to counteract an environment more conducive to microbiological activity.

Water Treatment Process Control: Delivered water quality versus water treatment parameters

Next, the water treatment system might influence the water quality that is seen at the high water age monitoring station and the state of the distribution system. It was seen from the metal plate analyses that aluminum hydroxide plays a role on lead plates. Monitoring station data show increasing aluminum concentration in the water. The use of alum, the aluminum compound used in water treatment, should be evaluated in the light of its effect after the treatment plant.


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Other treatment chemicals, not measured in this project, may also affect delivered water quality and seasonal changes in water production will change the amount and type of chemicals used in water treatment.

Water Treatment Process Control: Delivered water quality versus source water parameters

The seasonal changes of Lake Michigan water quality will also affect the quantity and type of chemicals used in water treatment, which in turn affects the consumers’ water quality. In this project, there was highly variable source water turbidity in winter responded to with increased alum dosing. Subsequently, this affects the precipitation of aluminum hydroxide in the distribution system and its possible effects on lead release.

In addition, Lake Michigan water quality is seen as changing over time with the stresses of interacting with civilization. These changes can also be the focus of monitoring, tracing the effects to the delivered water quality.

Comprehensive Perspective on Process Control

Many factors have an effect on delivered water quality to some degree whether or not they are measured in a monitoring project. There could be plenty of these factors that were missed because our knowledge was not great enough or our budget was not large enough. All that can be done is to watch the reaction results and try to determine a cause that can be translated into operational policy.

If there had been more time in this project, further monitoring would have been performed to determine the cause of the lead and copper increase in the summer time. The intentional drop in orthophosphate concentration would have been performed in cold weather months. The orthophosphate concentration would have been held steady during the next summer months to determine if the lead increase is a seasonal phenomenon. More scrutiny of the source water quality and the treatment chemicals, especially alum, would have provided more factors that might show an influence on the lead reaction.

As stated previously, this is an iterative empirical approach which shows the connectivity of factors throughout the water system leading all the way to the consumers’ taps. This monitoring and data analysis method make it possible to know how and to what degree natural and added chemicals in the water change from the distribution system entry point to the locations of high water age. We can also see and compare effects of chemical and microbiological reactions at these points in the distribution system. We can pinpoint atypical behavior for any parameter. And, we can watch parameter behavior when intentional changes are made in the system.

Routine gathering of the water system information, graphing of the information on Shewhart control charts, and discussion of the charts in operational, management, and planning meetings provide in-depth feedback on the operation of the water system. Taking action on behalf of system improvement and stability is the next step. Then, the cycle repeats. This is comprehensive consumer-centric process control.


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CHAPTER 5 GETTING STARTED WITH COMPREHENSIVE CONSUMER-CENTRIC PROCESS CONTROL

GETTING STARTED WITH SHEWHART CONTROL CHARTS

A Microsoft Excel® add-in is provided with this report to demonstrate the use of Shewhart control charts. Table 2.4 and Figure 2.7 show the calculations and charts produced with this add-in. The add-in is written for use with Microsoft Excel® 2007. Install the add-in by double-clicking the “setup.exe” file and following directions on the computer screen.

After the installation, the add-in controls will show up on the Excel® tab at the top of the screen called, “Insert”, under the “Control Chart” section.

To use the add-in, first create a data input spreadsheet for the control chart by clicking the button on the tab. You are given a choice whether to create the data input spreadsheet in a new Excel® file or to create a spreadsheet in an open Excel® file.

In the data input spreadsheet created by the add-in, enter the chart title and the y-axis label. For example, for charting disinfection data, the title of the chart might be “Disinfection at 123 Main Street” and the y-axis label, “Total Chlorine in mg/L”.

Finally, enter the date/result pairs with date in Column A and result in Column B. The data can be entered by hand or cut and pasted from another spreadsheet.

Click the “Create Control Chart” button on the Excel® tab. You are given a choice as to how many decimal places to show for the chart. Then, the charts and calculated statistics appear.

The top chart displayed is a Shewhart control chart with subgroup size one, also called an Individuals Chart. Water quality monitoring data can be used with this type of chart. The chart shows data plotted over time with control lines for 1, 2, and 3 calculated data variation units (sigma units) away from the average value. Sigma units can be used for any data set and take the place of the standard deviation units that can only be used for normally-distributed data.

The bottom chart is a Range Chart. It is a Shewhart control chart plotting the differences between successive data points. A three-sigma control limit is shown for the range chart. Discussion of the range chart is out of the scope of this project. More details can be found elsewhere (Wheeler and Chambers 1992). The formatting of both charts can be changed, if desired, using the functionality built into Microsoft Excel®.

The control limits gain more meaning with more data points used in the calculation of the statistics. After 10 data points, the statistics become more representative of the system being monitored; 20 or more data points create a better foundation for these estimates of system variance.

To interpret the Shewhart control charts, look for the following atypical patterns:

1. Data fall outside the 3 sigma unit lines 2. At least 2 out of 3 successive values fall on the same side of the average and are 2

sigma units or greater away from the average 3. At least 4 out of 5 successive values fall on the same side of the average and are 1

sigma unit or greater away from the average 4. 8 or more successive points fall on the same side of the average line


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If the atypical patterns occur, ask questions about the water system. Have any changes occurred on purpose or any unplanned events taken place? Note those changes on the control charts over the appropriate time period. GETTING STARTED WITH A COMPREHENSIVE CONSUMER-CENTRIC PROCESS CONTROL METHODOLOGY

Getting started on the concept of a comprehensive consumer-centric process control methodology in a water system can be a simple effort using readily available data. Steps include:

1. Use readily available water system data with Shewhart control charts to begin studying

data. a. For data on the consumers’ water quality, use existing regulatory distribution

system disinfection data collected around the distribution system for The Total Coliform Rule.

b. Use existing SCADA and on-line sensor data for other water system data. c. For each water quality parameter at each sampling site, enter the dates and data

values into a data entry spreadsheet of the Excel® add-in that comes with this report. (See the previous section for instructions.) Click the button to create the Shewhart control chart. Examples of disinfection data charted on Shewhart control charts at each NSWC Total Coliform Rule sampling site in the distribution system are shown in Figures 3.38 and 3.39. Figures 3.34 to 3.37 show examples of SCADA and on-line sensor data.

2. Evaluate the Shewhart control charts created in Step 1. a. When have any of the parameters exhibited a significant change from typical

water system operation as defined by the Shewhart control charts? (See the rules of chart interpretation in the previous section.)

b. Do the data exhibit any trends? c. Do parameters have a wide or narrow variation? d. Do average values of the parameters meet water system goals? e. What operational changes might bring parameters to the desired average values

and narrow the variability of the parameters? 3. Make operational changes proposed in Step 2 and continue to collect and study the

readily available water system data. a. Are the new goals being met? b. Adjust operations accordingly. c. Iterate steps 1, 2, and 3 for process improvement.

4. When comfortable with this method, carry out a more comprehensive monitoring strategy to build a bigger picture of delivered water quality.

a. Add the study of metal transfer and biostability to the monitoring strategy by using one or more monitoring apparatuses (derived from the pipe loop concept) strategically placed in the distribution system.

b. Join industry-wide water system improvement efforts, such as those promoted by the American Water Works Association, for encouragement and general guidance.


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5. Keep the monitoring efforts going. Figure 4.2 shows that a comprehensive consumer-centric process control methodology includes an iterative series of monitoring, evaluation, and decision-making. That is, collect data, create charts, apply the chart rules of interpretation, discuss charts in daily, weekly, and planning meetings, troubleshoot issues, make system changes for improvement, collect data, … etc. Keep the iterative process going to be rewarded with:




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CHAPTER 6 SUMMARY OF BENEFITS

The use of an apparatus that captures data representative of delivered water quality and the application of Shewhart control charts has many benefits immediately evident from this project as well as from similar projects at other water utilities. Some observed benefits are described below: SYSTEM OPERATION Triggering the need to troubleshoot equipment or other system operations

In one of the PRS Monitoring Station projects, disinfection dosage was found to fall very low at times. It was known that the chemical feed pump was troublesome, but the plotting of these events on a graph displayed visually the prevalence of this problem. Because of the awareness of the delicate balance of water quality parameters that is needed to achieve high water quality, every drop on the disinfection curve reminded the personnel that water quality is at risk at those times. A stronger effort was made to remedy the chemical feed pump problems. Saving money on treatment chemicals

In another PRS Monitoring Station project, corrosion control chemical dosage tracked at a high water age location exhibited trends that tended toward the upper control limit. This visual alert initiated a discussion about the operation of the chemical feed pumps and the intended dosage goal. The discussion also led to assessing the chemical feed costs only to find that the dosage per thousand gallons of water pumped had increased from the previous year. This translated into an excess of $23,000 in chemical costs. It is suspected that the water pumpage may have been lower that year compared to previous years while the constant setting of the chemical feed pump caused a higher dosage. Plans were made to change the pump control strategy. The use of on-going monitoring and routine creation and inspection of Shewhart control charts with dosage goals clearly in mind would have stopped this situation earlier and saved the utility a lot of money. Minimizing temporary water quality degradation

The routine measurement of turbidity and entrained iron concentration in one project painted a picture of the impact of system operating protocols and regular events. These events, such as water main breaks, connecting new service lines, water main flushing, and fluctuations in water chemistry, can lead to the disturbance of scales and films from pipe walls. As turbidity or other water quality parameters fluctuate toward or above upper control limits on a Shewhart control chart, it is evident that certain practices are significant in contributing to the temporary degradation of water quality. With this knowledge, and more importantly, with routine measurement, system protocols can be modified to lessen the disturbances. Alternatively, more effort can be made to clean scales and films from the pipelines and to keep the scales and films from accumulating again. With less temporary water quality degradation events, there is more overall consumer confidence.


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Evaluating system operations routinely

The use of Shewhart control charts gives visual signals to ask questions about the system operation. Even if no data points jump outside of the control limits and the system is considered in statistical control, the charts make it possible to set quantitative goals for moving the average value of the parameter being measured and for narrowing the variability of the results (tightening the control limits through operational changes). Questions are asked and ideas for operational improvements are developed. LEAD AND COPPER RULE ISSUES Addressing Lead and Copper Rule compliance issues scientifically

The previous examples of benefits are based on the monitoring of flowing water at critical points in the water system and do not require any special apparatus in order to gather information. Insight into the water system characteristics can go deeper with the use of a special apparatus to obtain representative samples from the distribution system and to allow the contact of lead or copper with water in a controlled environment.

Since 2006, the PRS Monitoring Stations have successfully been applied to Lead and Copper Rule issues by:

Studying lead and copper transfer to water in the context of the whole water

system with its multitude of influencing factors Serving as a surrogate for residential sampling so that lead and copper

concentration trends can be known routinely instead of being glimpsed at every three years

Aiding in the determination of the mechanism or mechanisms of lead and copper transfer into the water specific to the water system

Establishing key water system water quality parameters that are relevant to the individual water system instead of general parameters listed in a regulation to be applied to all water systems

Monitoring and controlling the key water system water quality parameters, especially those that can be controlled at the water treatment plant to keep them at the desired level with narrow variation; variation of key parameters can cause variation in lead and copper release to water

Monitoring and controlling system transitions, such as changes in corrosion control chemicals, changes in water sources, and changes in disinfection or other water treatment, in order to achieve simultaneous compliance with drinking water regulations

Determining the need for corrosion control chemicals Comparing and selecting corrosion control chemicals


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BEYOND LEAD AND COPPER RULE ISSUES

In monitoring lead and copper transfer into distribution system water routinely, it becomes evident that aspects of the water quality that transcend the Lead and Copper Rule issues can be illuminated. Assessing and controlling biostability of water, aiding in setting an appropriate disinfection concentration, and staying in compliance with the Total Coliform Rule

Non-pathogenic microorganisms play a significant role in water systems that has not been appropriately acknowledged in the drinking water community. Achieving biostability, that is, balancing disinfection with nutrients in the water, not only helps lower lead and copper and iron corrosion in a water system (the Lead and Copper Rule), but also makes the system less vulnerable to harm by pathogenic microorganisms (the Total Coliform Rule). Monitoring and control charts can be used to track and establish biostability. Assessing cleanliness of the piping system

Scales and films on pipe walls also play a significant role in distribution system water quality in terms of both chemistry and microbiology. Chemical scales can sorb other metals and release spikes of these metals. Biofilms on pipe walls are colonies of microorganisms at work releasing acidic enzymes that corrode metal and providing pioneers and nutrients to colonize elsewhere in the water system. Efficient cleaning of the pipes is necessary. Monitoring and control charts can be used to track the cleanliness of the water system. Developing hypotheses for further research on a system level and on a national level

It has been shown in this project how many questions are raised after studying the monitoring data. This leads to formulating and testing hypotheses in the water system to uncover cause and effect.

The monitoring data can also be used comparatively from water system to water system. A number of water utilities have used this philosophy of proactive monitoring with interpretation of results by means of Shewhart control charts since 2006. A wealth of data and control charts has been produced. These charts have initiated questions concerning current knowledge of water chemistry and microbiology. The data can be used at some future time as a basis for water research. The data are especially useful in that they are obtained under the same standard conditions at all sites in a water system and between water systems.

A COMPREHENSIVE CONSUMER-CENTRIC PROCESS CONTROL METHODOLOGY Establishing a water quality control methodology

The benefits listed previously add up to better water quality control. As presented in this report, a comprehensive process control methodology is desired based on the water quality delivered to the consumer. With this knowledge gathered routinely over time, the delivered


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water quality can be tied together with the operation of the distribution system, the water treatment facilities, and the source water pumping. Establishing a water system process improvement methodology

Besides tying the various aspects of water system operation to delivered water quality, the Shewhart control charts can aid in water system process improvement. The charts are used to guide management action that will move an average result to a desired average and to narrow the variation of the results for a more consistent system.

As was stated elsewhere and borrowed from Dr. W. Edwards Deming, the industrial quality control and process improvement consultant who promoted the Shewhart control chart techniques – “… a graph of a water quality parameter over time is ‘the process talking to us’. It is time that we listened.” (Cantor 2009, Deming 1993).


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APPENDIX A MICROBIOLOGICAL GROWTH INDICATOR

by Andrew D. Jacque, P.E., Ph.D. DESCRIPTION

Monitoring station test chambers containing metal plates (copper and lead, 2.5 inches square) were removed from the North Shore Water Commission water treatment facility and water distribution system on December 3, 2010 for biofilm sampling. Four test chambers, each containing two racks of eight plates each, were sampled. Sampling of Rack 1 from each test chamber was performed at the Wisconsin State Lab of Hygiene by Archie Degnan per previously established methods; Rack 2 was sent to Dr. Barry Maynard for metallurgical analysis.

Monitoring station test chambers were in service in the North Shore water system for about one year, with Station 1 located at the point of entry of the water system (treatment plant) and Station 2 located within the distribution system at a high water age point (system). Two test chambers contained lead plates, designated 1-Pb-1 and 2-Pb-1; two test chambers contained copper plates, designated 1-Cu-1 and 2-Cu-1. Test Chambers labeled 1-Pb-1 and 1-Cu-1 were located at the treatment plant (Station 1), and 2-Pb-1 and 2-Cu-1 were located in the distribution system (Station 2).

The microbial growth indicator (MGI) assay was performed on biofilm samples prepared by Archie Degnan. Work for the assay was performed by Dr. Andrew Jacque at the University of Wisconsin Platteville. The MGI assay utilized the tetrazolium salt XTT (2,3-bis[2-methyloxy- 4-nitro-5-sulfophenyl]-2H-tetrazolium-5-carboxanilide) and the electron shuttle Phenazine Methosulfate (PMS) to detect total electron activity, presumably associated with microbial respiration (Chaieb et al. 2011, McCluskey et al. 2005, Tsukatani et al. 2010). Electron activity can be measured spectophotometrically by quantifying the electron reduction of XTT to a formazan (XTF) and/or the electron shuttle Phenazine Methosulfate (PMS) to its semiquinone (SQ) form (PMS-SQ).

The objective of the MGI assay was to determine whether a correlation exists between heterotrophic plate count data (HPC) and total electron activity within a water sample using the MGI assay. PROCEDURE

10 ml aliquots of suspended biofilm from each monitoring station test chamber were obtained for use in the MGI Assay. Each suspension was split, with half of the suspension filtered through a 0.22 m filter to remove biological and particulate matter. Two plates from each test chamber were obtained for whole plate analysis by the MGI Assay.

Solutions of XTT (14.8 mM) and PMS (0.392 mM) were prepared in de-ionized (DI) water under limited light exposure. Both chemicals are light sensitive, and can be photo-reduced by UV radiation. The XTT solution was incubated in dark at 37C for 30 minutes to promote full dissolution. The MGI assay consists of a XTT-PMS mixture with PMS acting as the electron shuttle between microbial respiration and the XTT dye. The PMS assay, which investigates electron shuttle activity, contains only PMS.

For the MGI assay on an unfiltered sample, 1.0 ml of sample, 0.8 ml of XTT stock solution, 0.8 ml of PMS stock solution and 3.0 ml of DI were added to a 12.5 ml glass tube.


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With the filtered sample, 1.0 ml of filtered sample, 0.8 ml of XTT stock solution, 0.8 ml of PMS stock solution and 3.0 ml of DI were added to a 12.5 ml glass tube. A plate of each type from each location was separately placed in DI with XTT and PMS added for a final volume of 20 ml. Final concentration of XTT was 2.1 mM and PMS was 56 M.

For the PMS assay on an unfiltered sample, 1.0 ml of sample, 0.8 ml of PMS stock solution and 3.8 ml of DI were added to a 12.5 ml glass tube. With the filtered sample, 1.0 ml of filtered sample, 0.8 ml of PMS stock solution and 3.8 ml of DI were added to a 12.5 ml glass tube. A plate of each type from each location was separately placed in DI with PMS added for a final volume of 20 ml. Final concentration of PMS was 56 M in all samples.

Blanks were prepared in simplex for each assay (MGI and PMS), with sample volume replaced by the addition of 1.0 ml of DI to maintain comparative chemical concentration between sample and blank.

Each assay was performed in duplicate for each test chamber analyzed. Assays were incubated in dark at 37C for 120 minutes.

After the 120 minute incubation period, absorbance measurements of each sample and blank were acquired by direct reading of the sample tubes using a BioMate 3 spectrophotometer. Samples were handled so as to not mix or disturb the solution within the tubes. Measurements were taken at wavelengths of 387 nm, 446 nm and 470 nm, which measure the presence of PMS (oxidized form), PMS-SQ (reduced form) and XTT-F (reduce form) respectively. A 5 ml aliquot of the assay solution from each plate assay was transferred to a 12.5 ml glass tube for absorbance measurements. Relative color of each sample was also noted at the time of analysis. RESULTS

Results for the unfiltered MGI assay are depicted in Figure A.1. The MGI assay showed that the amount of PMS and PMS-SQ in solution increased for each test chamber location and plate type, with XTT-F decreasing for copper plates and increasing for lead plates. The copper sample assays turned a yellowish-black color after 2 hours of incubation and purple after 24 days of incubation, whereas the lead sample assays turned a yellowish-brown color after 2 hours of incubation and an orange color after 24 days of incubation.

These results are not completely as expected. The electron reduction of PMS to PMS-SQ should result in a decrease in PMS and an increase in PMS-SQ and XTT-F. This result did not occur for the copper plate test chambers, and the results are inconclusive for the lead plate test chambers. The electron reduction of XTT to XTT-F should result in the development of an orange color, which was seen in the lead plate test chambers. The development of a purple color for the copper test chambers suggests the formation of a copper-formazan product, which may have resulted in a smaller concentration of XTT-F and an absorbance shift of the XTT-Cu-F product.

Results for the unfiltered PMS assay are depicted in Figure A.2. The PMS assay showed that the amount of PMS in solution decreased and PMS-SQ increased for each test chamber location and plate type. All assays maintained a light yellow color after 2 hours and 24 days of incubation. These results follow expectations and show electron activity in the samples, and further suggest that metals in solution may interfere with the MGI assay.


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Note: This shows measured change in absorbance between sample and blank. A positive absorbance change indicates an increase in concentration in sample compared to blank. Absorbance at 387 nm represents the oxidized compound PMS, which acts as the electron shuttle, absorbance at 446 nm represents the reduced semiquinone form of PMS (PMS-SQ), and absorbance at 470 nm represents the XTT-formazan produced by reduction of XTT by PMS-SQ. Error bar represents one standard deviation.

Figure A.1 Results for unfiltered MGI assay

Note: This show measured change in absorbance between sample and blank. A positive absorbance change indicates an increase in concentration in sample compared to blank. Absorbance at 387 nm represents the oxidized compound PMS, which acts as the electron shuttle. Absorbance at 446 nm represents the reduced semiquinone form of PMS (PMS-SQ). Error bar represents one standard deviation. Figure A.2 Results for unfiltered PMS assay


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Results for the filtered MGI assay are depicted in Figure A.3. This assay removes the presence of microorganisms and thus represents changes in the assay associated with dissolved constituents in the background water. The filtered MGI assay showed that the amount of PMS, PMS-SQ and XTT-F in solution generally decreased for each test chamber location and plate type. The copper sample assays turned a yellowish-grey color after 2 hours of incubation and reddish-orange after 24 days of incubation, whereas the lead sample assays turned a yellowish-brown color after 2 hours of incubation and an orange color after 24 days of incubation. These results are not as expected. While PMS should show a decline if electron activity if it is present, there should be a corresponding increase in PMS-SQ. In addition, the large reduction of absorbance for XTT-F suggests removal of XTT from the system. These results further suggest that the presence of dissolved metals may interfere with the MGI assay.

Note: This shows measured change in absorbance between sample and blank. A positive absorbance change indicates an increase in concentration in sample compared to blank. Absorbance at 387 nm represents the oxidized compound PMS, which acts as the electron shuttle, absorbance at 446 nm represents the reduced semiquinone form of PMS (PMS-SQ), and absorbance at 470 nm represents the XTT-formazan produced by reduction of XTT by PMS-SQ. Error bar represents one standard deviation. Figure A.3 Results for filtered MGI assay

Comparison of the filtered MGI assay to the MGI assay, which is the absorbance values associated with the MGI assay minus the absorbance values associated with the filtered MGI assay, is shown in A.4. This comparison assesses the amount of electron activity associated with microbial occurrence, and shows that XTT-F was formed by microbial respiration in both the copper and lead plate test chambers.

Results for the filtered PMS assay are depicted in A.5. This assay removes the presence of microorganisms and thus represents changes in the assay associated with background water. The filtered PMS assay showed that the amount of PMS and PMS-SQ in solution generally decreased for each test chamber location and plate type. All assays maintained a light yellow color after 2 hours and 24 days of incubation. These results are not as expected. While PMS should show a decline if electron activity if it is present, there should be a corresponding increase in PMS-SQ. These results further suggest that the presence of dissolved metals may interfere with the MGI assay.


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Note: This shows the change in absorbance between the two methods. Absorbance at 387 nm represents the oxidized compound PMS, which acts as the electron shuttle, absorbance at 446 nm represents the reduced semiquinone form of PMS (PMS-SQ), and absorbance at 470 nm represents the XTT-formazan produced by reduction of XTT by PMS-SQ. Error bar represents one standard deviation. Figure A.4 Results for comparison of MGI assay with filtered MGI assay

Note: This shows measured change in absorbance between sample and blank. A positive absorbance change indicates an increase in concentration in sample compared to blank. Absorbance at 387 nm represents the oxidized compound PMS, which acts as the electron shuttle; absorbance at 446 nm represents the reduced semiquinone form of PMS (PMS-SQ). Error bar represents one standard deviation. Figure A.5 Results for filtered PMS assay


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Comparison of the filtered PMS assay to the PMS assay, as shown in Figure A.6, assesses the amount of electron activity associated with microbial occurrence. This comparison shows that PMS was formed by microbial respiration in both the copper and lead plate test chambers as the presence of PMS-SQ increased relative to PMS, and is consistent with the same filtered sample analysis for the MGI assay.

Results for the MGI plate assay are depicted in A.7. This assay tests electron activity of an intact biofilm on a plate immersed in the assay solution. These assay results show that electron activity was occurring on the plates, and that the activity associated with the plates was larger than that associated with the biofilm suspension. Interestingly, the copper plates, which showed elevated HPC but low iron related bacteria occurrence, exhibited low electron activity, whereas the system lead plate, which showed low HPC but elevated iron related bacteria occurrence, showed high electron activity.

Results for the PMS plate assay are depicted in Figure A.8. This assay tests electron activity of an intact biofilm on a plate immersed in the assay solution. The assay results show that electron activity was occurring on the plates with the large drop in PMS concentration relative to PMS-SQ.

Note: This shows change in absorbance between the two methods. Absorbance at 387 nm represents the oxidized compound PMS, which acts as the electron shuttle; absorbance at 446 nm represents the reduced semiquinone form of PMS (PMS-SQ). Error bar represents one standard deviation.

Figure A.6 Results for comparison of PMS assay with filtered PMS assay


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Note: This shows measured change in absorbance between sample and blank. A positive absorbance change indicates an increase in concentration in sample compared to blank. Absorbance at 387 nm represents the oxidized compound PMS, which acts as the electron shuttle, absorbance at 446 nm represents the reduced semiquinone form of PMS (PMS-SQ), and absorbance at 470 nm represents the XTT-formazan produced by reduction of XTT by PMS-SQ. Figure A.7 Results for MGI assay on 2.5” square plates

Note: This shows measured change in absorbance between sample and blank. A positive absorbance change indicates an increase in concentration in sample compared to blank. Absorbance at 387 nm represents the oxidized compound PMS, which acts as the electron shuttle. Absorbance at 446 nm represents the reduced semiquinone form of PMS (PMS-SQ). Figure A.8 Results for PMS assay on 2.5” square plates


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The tetrazolium salt CTC (5-cyano-2,3-ditolyl tetrazolium chloride) has been shown to detect respiratory activity within drinking water biofilm and bulk water samples, however only about 1% of planktonic and 25% of sessile organisms are able to reduce CTC to a formazan. This method also showed that HPCs underestimate microbial occurrence by two to three orders of magnitude. While the CTC assay appears promising, it requires the use of a fluorometer, which is a specialized instrument that may not be readily accessible. The MGI assay sets out to provide an easier to perform and rapid assessment for microbial occurrence.

Results for the MGI assay suggest that microbial occurrence can be quickly detected, although the presence of dissolved constituents (metals) may interfere with it. To account for this interference, background analysis of electron activity should be included by splitting the sample and analyzing the filtrate. Comparison of the two assays, by taking the absorbance values associated with the MGI assay minus the absorbance values associated with the filtered MGI assay, will give a representative assessment of total electron activity associated with microbial occurrence. A true association of the number of microbes present per unit change in absorbance will be difficult to establish due to the diverse population that could be present and the associated physiology. It does appear that iron related bacteria result in a larger measure of electron activity, which involves organisms that typically do not show up on an HPC. Further research in this area is needed, or research into use of ATP measurement as an indicator of active microbial occurrence, given the nature of sample interference with the MGI assay.


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APPENDIX B METAL PLATE BIOFILM ANALYSIS

by Archie Degnan, Advanced Microbiologist

DESCRIPTION

Monitoring station test chambers containing metal plates (2.5 x 2.5 inches), were removed from North Shore Water Commission system for biofilm sampling, one year after installation. The metal plates represented pipe wall materials, but were relatively easy to remove and sample without disrupting the water utility infrastructure. Four test chambers containing eight plates each were sampled. Two test chambers contained lead (1-Pb-1; 2-Pb-1) and two contained copper (1-Cu-1; 2-Cu-1) plates. Assays performed at the Department of Water Microbiology Wisconsin State Laboratory of Hygiene included 1.) biofilm heterotrophic plate counts (HPC; R2A method) 2.) iron bacteria counts, and 3.) sulfate-reducing bacteria (presence or absence).

PROCEDURE The procedure for separating biofilm from metal plates is described in detail in Water Distribution System Monitoring (Cantor 2009). Details for procedures for HPC, iron bacteria, and sulfate-reducing bacteria are described in Standard Methods for the Examination of Water and Wastewater (APHA et al., 2005). The four test chambers were disconnected from the North Shore station around 8:00 a.m. on December 3, 2010, and transported via automobile to the Wisconsin State Laboratory of Hygiene. Upon arrival at around 11:00 a.m., laboratory procedures for removing biofilm from metal surfaces began. Test chamber bolts were removed to expose the rack which secures the eight plates. Six of the eight plates were aseptically transferred to a 500 mL Nalgene screw-cap container (two plates remained in the test chamber for delivery to co-investigator Andrew Jacques for further testing). Two hundred mL of sterile phosphate buffer were then added to the Nalgene container, and the cap was securely tightened. Biofilm was dislodged from the metal plates by agitating for 2 minutes using a Multi-Wrist Shaker, followed by 2 minutes sonication.

Following biofilm dislodging, serial dilutions of the 200 mL phosphate buffer were transferred to Petri plates (1.0 mL per plate), after which R2A media, tempered to 48oC, was added and allowed to solidify at room temperature (about 20 minutes). Petri dishes were then stored at 20oC for up to 7 days, after which colonies were counted and the following calculation applied to quantify initial HPC populations (colony forming units [CFU]/square inch) on the plates.

HPC/in.2 = colony forming unit count x dilution factor x 200 mL/6 plates x 6 plates/75 in.2 Final HPC counts appear in Table B.1. For iron bacteria detection, 1 mL phosphate buffer containing dislodged biofilm (use of greater volume was restricted by turbidity of liquid) was vacuum-filtered through a 45 micron cellulose filter. The filters were then viewed under 1000x magnification and identification and quantification of iron bacteria were confirmed by Advanced Microbiologist Jeremy Olstadt. The following equation was used to quantify;


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Iron bacteria/ml = C x A1/Afc x V where C = bacteria count A1 = total microscope fields per filter Afc = fields viewed by technician V = volume filtered (mL)

Final iron bacteria counts appear in Table B.2. For detection of sulfate-reducing bacteria, 2 mL phosphate buffer containing dislodged biofilm were combined with 8 mL sulfate-reducing-bacteria media, tempered to 48oC, in a screw-cap glass test tube. The tubes are stored at room temperature (20oC) for 21 days, after which a color change from milky white to a black precipitate indicates the presence of sulfate-reducing bacteria. Results are reported as either present or absent, and appear in Table B.3. RESULTS

Table B.1 Heterotrophic plate counts (HPC)

Total Sample plate #1 plate #2 average dilution factor (HPC/in.2) 1-Cu-1 82 71 77 100 7,700 2-Cu-1 47 41 44 1000 44,000 1-Pb-1 12 9 11 1 11 2-Pb-1 33 31 32 10 320

Table B.2 Iron bacteria counts

Total Sample cell count vol. filtered fields counted (cells/mL) (cells/in.2) 1-Cu-1 1 1 175 8 13 2-Cu-1 1 1 210 6 16 1-Pb-1 4 1 35 152 410 2-Pb-1 2 1 20 133 359 Note: based on 1,330 fields per filter


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Table B.3 Sulfate-reducing bacteria (SRB; presence / absence)

Sample SRB 1-Cu-1 absent 2-Cu-1 absent 1-Pb-1 absent 2-Pb-1 absent


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APPENDIX C MICROBIOLOGICAL INSPECTION OF METAL PLATES

by Andrew D. Jacque, P.E., Ph.D. DESCRIPTION

Monitoring station test chambers containing metal plates (copper and lead, 2.5 inches square) were removed from the North Shore Water Commission water treatment facility and water distribution system on December 3, 2010 for biofilm sampling. Four test chambers, each containing two racks of eight plates each, were sampled. Sampling of Rack 1 from each test chamber was performed at the Wisconsin State Lab of Hygiene by Archie Degnan per previously established methods; Rack 2 was sent to Dr. Barry Maynard for metallurgical analysis.

Monitoring station test chambers were in service in the North Shore water system for one year, with Station 1 located at the point of entry of the water system (treatment plant) and Station 2 located within the distribution system at a high water age point (system). Two test chambers contained lead plates, designated 1-Pb-1 and 2-Pb-1; two test chambers contained copper plates, designated 1-Cu-1 and 2-Cu-1. Test Chambers labeled 1-Pb-1 and 1-Cu-1 were located at the treatment plant (Station 1), and 2-Pb-1 and 2-Cu-1 were located in the distribution system (Station 2).

PROCEDURE

The four test chambers were disconnected from the North Shore station around 8:00 a.m. on December 3, 2010, and transported via automobile to the Wisconsin State Laboratory of Hygiene. Upon arrival at around 11:00 a.m., test chamber bolts were removed to expose the rack which secures the eight plates in each test chamber. Six of the eight plates were removed for transfer of biofilms to water for analysis (Appendix B). Two plates remained in each test chamber and were taken to the University of Wisconsin-Platteville laboratory for inspection by scanning electron microscope (SEM). RESULTS

Figures C.1 through C.8 are SEM photos of lead plates removed from the distribution system monitoring station.

Figure C.1 is a low magnification (50X) view of the area under a spacer ring that separates the plates in the rack used to mount the plates in the monitoring station. Bolt hole through middle of the plate is in the upper right of the figure. Figure C.2 and C.3 are 5000X magnification views of the box in the middle of Figure C.1. Note the large amount of extracellular polymeric substances (EPS) in Figure C.2 and C.3 (indicated with arrows) that resulted from microbial occurrence. Mineral in background appears to be cerussite.

Figure C.4 is a 500X magnification view near Figure C.1. Figure C.5 is a 2000X magnification view of the box in the middle of Figure C.4. Figure C.6 is a 5000X view of the box to the lower right of the middle of Figure C.4. Note the large amount of EPS in Figure C.5 and C.6 (indicated with arrows) that resulted from microbial occurrence. Mineral in background appears to be cerussite.


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Figure C.7 is a low magnification view of a portion of a lead plate exposed to flowing water within the monitoring station. Figure C.8 is a 5000X view of the boxed region in Figure C.7. Note that the entire surface within this view is covered with EPS.

The above figures confirm the MGI assay results and the HPC and iron related bacterial analyses, which showed that microorganisms were present within the monitoring station.

Figures C.9 through C.14 are SEM photos of copper plates removed from the treatment plant monitoring station.

Figure C.9 and C.10 are 5000X magnification views of the area under a spacer ring that separates the plates in the rack used to mount the plates in the monitoring station. Note the large amount of extracellular polymeric substances (EPS) in Figure C.9 and C.10 (indicated with arrows) that resulted from microbial occurrence.

Figure C.11 is a low magnification (50X) view of the area under a spacer. The bolt hole through middle of the plate is to the lower right in the figure. Figure C.12 is of the boxed area in Figure C.11, which shows the formation of a biofilm. Figure C.13 and C.14 are in the vicinity of Figure C.11 and also show the formation of a biofilm. Note the radiating structure of the biofilm, suggesting the presence of a filamentous or stalk forming bacterium. Figure C.14 also shows the presence of microorganisms encased in EPS (indicated with arrows).

Figure C.15 and C.16 are close-up views of the radiating ribbon-like stalk structures shown in Figure C.14.

Biofilms shown in the above figures are likely immature and may change considerably in appearance when compared to well-established biofilms in drinking water systems. However, established biofilms picked from water system aged copper pipes exhibited similar EPS structures shown in Figure C.14 (From a 2010 study by Dr. Jacque).


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Note: A small region is shown in the middle of the photo that was studied with higher magnification (Figures C.2 and C.3). Figure C.1 SEM of lead plate under spacer with 50X magnification


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Note: Arrows point to extracellular polymeric substances present from microbiological activity Figure C.2 SEM of boxed region shown in Figure C.1 with 5000X magnification


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Note: Arrows point to extracellular polymeric substances present from microbiological activity Figure C.3 SEM of boxed region shown in Figure C.1 with 5000X magnification


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Note: Two boxed regions, shown on the photo, that were studied with higher magnification (Figures C.5 and C.6). Figure C.4 SEM of lead plate under spacer with 500X magnification


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Note: Arrows point to extracellular polymeric substances present from microbiological activity Figure C.5 SEM of upper boxed region shown in Figure C.4 with 2000X magnification


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Note: Arrows point to extracellular polymeric substances present from microbiological activity Figure C.6 SEM of lower boxed region shown in Figure C.4 with 5000X magnification


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Note: A boxed region is shown in the middle of the photo that was studied with higher magnification (Figure C.8). Figure C.7 SEM of lead plate exposed to water flow with low magnification


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Note: Area covered with extracellular polymeric substances present from microbiological activity Figure C.8 SEM of boxed region shown in Figure C.7 with 5000X magnification


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Note: Arrows point to extracellular polymeric substances present from microbiological activity Figure C.9 SEM of copper plate under spacer with 5000X magnification


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Note: Arrows point to extracellular polymeric substances present from microbiological activity Figure C.10 SEM of copper plate under spacer with 5000X magnification


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Note: A boxed region is shown in the middle of the photo that was studied with higher magnification (Figure C.12). Figure C.11 SEM of copper plate under spacer with 50X magnification


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Figure C.12 SEM of Boxed Region Shown in Figure C.11 with 500X magnification


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Figure C.13 SEM of copper plate under spacer


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Note: Arrows point to extracellular polymeric substances present from microbiological activity Figure C.14 SEM of copper plate under spacer


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Figure C.15 SEM of copper plate under spacer


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Figure C.16 SEM of lead plate under spacer with 10000X magnification


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APPENDIX D METALLURGICAL INSPECTION OF METAL PLATES

By J. Barry Maynard, Ph.D. (Professor of Geology) and David Mast, Ph.D. (Professor of Physics) – University of Cincinnati

DESCRIPTION

Metal plates were submitted from four PRS Monitoring Stations, two racks each of lead and copper with eight metal plates per rack. Visually, the lead plates could be seen to have a relatively even coating of white scale, with some additional scale types developed under the spacer in the center of the plate. The copper plates by contrast have thinner, patchy scale development.

PROCEDURE

Metal plates were analyzed by scanning electron microscope (SEM), X-ray Diffraction (XRD), X-Ray Fluorescence (XRF), and Raman spectroscopy as described elsewhere (Cantor 2009). RESULTS

SEM observations show the presence of lead oxides, carbonates, and phosphates on the lead plates; copper carbonates are observed on the copper plates. No sulfates were seen on the copper, unlike most cases of copper corrosion. Considerable aluminum was found on the lead plates, but not on the copper. Bulk chemistry by XRF reveals the presence of appreciable aluminum, calcium, and phosphorus in the scales on the lead plates. Amounts of aluminum and phosphorus were higher on plate 2-Pb-1 than on plate 1-Pb-1 and calcium was about the same. The copper plates lacked the aluminum found on the lead plates, a difference that was also seen in the SEM data, but like the lead plates, had appreciable calcium and phosphorus. In the copper case, however, plates 1-Cu-1 and 2-Cu-1 did not differ significantly in scale composition.

Bulk mineralogy by XRD showed the presence of pyromorphite, hydrocerussite, and calcite plus minor cerussite on the lead plates. The copper plates showed only the copper oxide cuprite. The copper carbonates seen in SEM are not present in sufficient amounts to be seen in the X-ray patterns (the limit of detection is between 5 and 10%). For lead, plate 2-Pb-1 has substantially greater scale development than plate 1-Pb-1, although both have the same minerals and proportions of those minerals. For copper, plate 2-Cu-1 has somewhat more cuprite than plate 1-Cu-1, but the ratios are less than in the lead case. No highly oxidized phases such as plattnerite (PbO2) or tenorite (CuO) were found in the x-ray patterns, and so comprise less than 10% of the scale.


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1Pb1 2Pb1

1Cu1 2Cu1 Figure D.1 General appearance of PRS Monitoring Station lead and copper plates after one year exposure to NSWC water


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Plate 1 Pb 1

Element Wt% At% O 19.60 70.04 Al 02.45 05.19 P 02.08 03.84 Pb 75.87 20.94 Matrix Correction ZAF

Element Wt% At% O 17.09 62.01 Fe 05.87 06.11 Al 02.89 06.21 P 02.41 04.52 Pb 70.84 19.85 Ca 00.90 01.31 Matrix Correction ZAF

Notes:

1. Top: lead phosphate + lead carbonate + aluminum hydroxide (carbon not measured). 2. Bottom: lead phosphate + lead carbonate + aluminum hydroxide + iron oxyhydroxide +

Calcite (carbon not measured) Figure D.2 SEM analysis of Plate 1-Pb-1.


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Plate 1 Pb 1



Notes:

1. Top: High magnification view of area in previous image. 2. Bottom: Similar area – bundles of fibers characteristic of pyromorphite

Figure D.3 SEM analysis of Plate 1-Pb-1.


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Plate 1 Pb 1

Element Wt% At% O 20.93 69.15 Al 03.25 06.36 P 02.69 04.60 Pb 71.96 18.36 Ca 01.17 01.54 Matrix Correction ZAF

Element Wt% At% O 20.74 63.15 Fe 07.86 06.85 Al 04.70 08.48 P 03.37 05.30 Pb 61.99 14.58 C 01.34 01.63 Matrix Correction ZAF

Notes:

1. Top: Higher magnification view of fibrous texture mineral. 2. Bottom: High aluminum portion of the sample

Figure D.4 SEM analysis of Plate 1-Pb-1.


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Plate 2 Pb 1

Element Wt% At% C 04.56 32.44 O 05.72 30.54 Pb 89.72 37.02 Matrix Correction ZAF

Notes: 1. Top: Photograph of plate showing concentric rings of minerals from under the Teflon

spacer. The red mineral in position 4 is litharge, a soluble lead oxide. 2. Bottom: SEM image and EDAX of spot 1 on previous image. Texture and compostion

indicates a lead carbonate.

Figure D.5 SEM Analysis of Plate 2-Pb-1.


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Plate 2 Pb 1



Note: Spots 2 and 3 on map image. Both are lead carbonate Figure D.6 SEM Analysis of Plate 2-Pb-1.


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Plate 2 Pb 1

Element Wt% At% CK 02.16 19.24 OK 04.93 32.88 PbL 92.91 47.88 Matrix Correction ZAF


Notes:

1. Top: Litharge (PbO) in spot 4. 2. Bottom: lead carbonate that covers the outer ring under the spacer.

Figure D.7 SEM Analysis of Plate 2-Pb-1.


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Plate 2 Pb 1

Element Wt% At% CK 07.24 35.67 OK 11.10 41.02 PbL 81.66 23.31

Matrix Correction ZAF

Note: Typical texture of the remainder of the lead plate. Dominant mineral is lead phosphate (phosphorus was omitted from the EDAX scan). Figure D.8 SEM Analysis of Plate 2-Pb-1.


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Plate 1 Cu 1

Element Wt% At% C 47.27 76.89 O 07.03 08.59 Mg 00.14 00.11 Al 00.30 00.22 Si 00.49 00.34 Ca 00.31 00.15 Fe 00.60 00.21 Cu 43.85 13.48 Matrix Correction ZAF

Element Wt% At% C 22.30 49.69 O 14.06 23.51 Cu 63.64 26.80 Matrix Correction ZAF

Note: Views of typical scale at low and high magnification showing well-developed crystals of copper carbonate. Lack of symmetry across the front is consistent with monoclinic crystals such as malachite or azurite. Note the crystal on the bottom appears to be suffering from dissolution. Figure D.9 SEM analysis of Plate 1-Cu-1.


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Plate 2 Cu 1



Notes:

1. Top: Crystals with typical pseudo-hexagonal outline of malachite. 2. Bottom: The higher ratio of carbon to copper in this analysis suggests azurite instead of

malachite Figure D.10 SEM analysis of Plate 2-Cu-1.


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Table D.1 Scale chemistry by X-Ray Fluorescence

Values in wt %.

Al Pb Zn Cu Ni Fe Mn Ca S P 1Pb1 3.81 61.9 0.003 0 0.001 0.188 0.012 7.99 0.0 8.82Pb1 16.24 59.6 0.020 0 0.000 0.123 0.018 7.98 0.0 11.1 1Cu1a 0.0 0.004 0.004 94.7 0.0 0.0 0.000 1.49 0.02 3.761Cu1b 0.0 0.003 0.004 94.2 0.0 0.0 0.003 1.51 0.04 4.181Cu1c 0.0 0.001 0.006 94.2 0.0 0.0 0.005 1.59 0.04 4.07

avg 1Cu1 0.0 0.003 0.005 94.4 0.0 0.0 0.003 1.53 0.03 4.00 2Cu1a 0.0 0.002 0.001 95.1 0.0 0.0 0.001 1.46 0.03 3.362Cu1b 0.0 0.005 0.001 94.1 0.0 0.0 0.001 1.77 0.06 3.992Cu1c 0.0 0.003 0.000 94.3 0.0 0.0 0.000 1.72 0.04 3.86

avg 2Cu1 0.0 0.003 0.001 94.5 0.0 0.0 0.000 1.65 0.04 3.74

Table D.2 X-Ray Diffraction intensities, normalized to underlying metal

Mineral Formula 1Cu1 2Cu1 2Cu1/1Cu1 Cuprite Cu2O 759 946 Cu 6348 5674 Ratio mineral/Cu 0.120 0.167 1.39

Mineral 1Pb1 2Pb1 2Pb1/1Pb1 Pyromorphite Pb5(PO4)3OH 986 745 Pb 553 75 Ratio mineral/Pb 1.783 9.933 5.57Calcite CaCO3 356 334 Pb 553 75 Ratio mineral/Pb 0.644 4.453 6.92Hydrocerussite Pb3(CO3)2(OH)2 246 365 Pb 553 75 Ratio mineral/Pb 0.445 4.867 10.94


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Raman spectra were taken at various locations on two of the Pb plates, one each of 2-Pb-1 and 1-Pb-1. Specific peaks for three lead based minerals were observed: lead carbonate, litharge and plattnerite. A broad luminescence, characteristic of lead phosphates, was also observed. The white minerals on the plates are identified as a mixed lead carbonate, ie. cerussite and hydrocerussite; see Figure D.11 for the Raman spectra of the white minerals on the plates. Figure D.12 shows the Raman spectrum of a sample of cerussite. The dark mineral located at the center of each plate around the center hole is lead oxide, primarily litharge, PbO. The Raman spectrum from this mineral is shown in Figure D.13. There is also evidence that minor amounts of plattnerite, PbO2, are present on the plate surfaces, as shown by Figure D.14.

Figure D.11 Raman spectrum of lead carbonate observed on lead plate 1-Pb-1

Figure D.12 Raman spectrum of a cerussite standard


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Raman spectra of the dark mineral around the center hole show a pronounced peak at ~250 cm-1 which we believe is the edge of the prominent peak at 142 cm-1 not fully captured in these spectra due to low wavenumber cutoff of the particular spectrometer that was used; note the steep drop in signal intensity below ~250cm-1. The peak around 350 cm-1 is the upper peak of litharge.

Figure D.13 Raman spectrum of dark red mineral identified as litharge

There is evidence that there might be a thin layer of lead IV oxide, plattnerite, also

present in these corrosion scales (see Figure D.14). This spectrum was obtained using a high power microscope objective an high laser power so that the lead(IV) oxide (which has a very weak signal and therefore would be unobservable if present as a thin layer of material) is photo-transformed into a mixed valance state oxide which has a very pronounced Raman peak near 285 cm-1. The lead carbonate peaks are also still observed showing that both minerals are covering the same surface of the metal plate. The broad feature in the spectrum is attributed to un-identified phosphate minerals. Figure D.15 shows the Raman spectrum from a pure lead (IV) oxide sample that has undergone photo-transformation to the mixed valance state lead oxide material.


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Figure D.14 Raman spectrum from plate set 2-Pb-1 showing the photo-transformed lead (IV) oxide peak around 300 cm-1

500 1000 1500 2000Raman Shift (cm-1)

0

10000

20000

30000

40000

50000

60000

70000

Co

un

ts

Figure D.15 Raman spectrum of photo-transformed pure lead (IV) oxide

PbIV oxide

PbII phosphate


167

REFERENCES

American Public Health Association (APHA), American Water Works Association (AWWA), and Water Environment Federation (WEF). 2005. Standard Methods for the Examination of Water and Wastewater, 21th ed. Washington, DC: American Public Health Association.

American Public Works Association (APWA), American Water Works Association, National Association of Water Companies, National Association of Clean Water Agencies, U.S. Environmental Protection Agency, Water Environment Federation, Association of Metropolitan Water Agencies. 2008. Effective Utility Management: A Primer for Water and Wastewater Utilities.

American Water Works Association (AWWA). 2011. Utility Quality Programs. [online]. Availble:

<http://www.awwa.org/Resources/utilitymanage.cfm?ItemNumber=3764&navItemNumber=1585>. [cited August 22, 2011].

American Water Works Association Research Foundation (AwwaRF) and DVGW-Technologiezentrum Wasser (DVGW-TZW). 1996. Internal Corrosion of Water Distribution Systems, 2nd ed. Denver: American Water Works Association Research Foundation/American Water Works Association.

Bremer, P.J., B.J. Webster, and D.B. Wells. 2001. Biocorrosion of copper in potable water. J. AWWA 93:8:82.

Boyd, G.R., McFadden, M.S., Reiber, S.H., Sandvig, A.M., Korshin, G.V., Giani, R. and Frenkel, A.I. 2010. Effect of changing disinfectants on distribution system lead and copper release: Part 2 - Research Results. Denver:Water Research Foundation.

Cantor, A.F., D. Denig-Chakroff, R.R. Vela, M.G. Oleinik, and D.L. Lynch. 2000. Use of polyphosphate in corrosion control. J. AWWA 92:2:95.

Cantor, A.F., J.K. Park, and P. Vaiyavatjamai. 2003. Effect of chlorine on corrosion in drinking water systems. J. AWWA 95:5:112.

Cantor, A.F. 2006. Diagnosing corrosion problems through differentiation of metal fractions. J. AWWA 98:1:117.

Cantor, A.F. 2008. Pipe Loop on Steroids! A New Approach to Monitoring Distribution System Water Quality. Proceedings of the American Water Works Association 2008 Water Quality and Technology Conference. Denver: AWWA.

Cantor, A.F. (2009). Water Distribution System Monitoring: A Practical Approach for Evaluating Drinking Water Quality. Boca Raton: CRC Press.

Cantor, A.F. and J. Cantor. 2009. In Dr. Deming’s Footsteps: Distribution System Water Quality Control and Process Improvement. Proceedings of the American Water Works Association 2009 Water Quality and Technology Conference. Denver: AWWA.

Cantor, A.F. 2010. Proactive Detection and Response to Lead and Copper in Drinking Water. Proceedings of the American Water Works Association 2010 Annual Conference and Exposition. Denver: AWWA.

Cantor, A.F. 2011. The Lead and Copper Rule: Proactive Detection and Response Are Key to Compliance. Opflow 37:1:14.

Cartier, C., L. Laroche, E. Deshommes, S. Nour, G. Richard, M. Edwards, and M. Prevost. 2011. Investigating dissolved lead at the tap using various sampling protocols. JAWWA 103:3:55.


168

Chaieb, K., Zmantar. T., Souiden, Y., Mahdouani, K., Bakhrouf, A. 2011. XTT assay for evaluating the effect of alcohols, hydrogen peroxide and benzalkonium chloride on biofilm formation of Staphylococcus epidermidis. Microbial Pathogenesis 50, 1-5.

Code of Federal Regulations. (CFR). Chapter 40 Protection of the Environment; Part 141 – National Primary Drinking Water Regulations; Subpart I – Control of Lead and Copper. Washington, DC: US Government Printing Office. (The complete text of The Lead and Copper Rule and other drinking water regulations can be found at the Internet web page, http://www.gpoaccess.gov/CFR/INDEX.HTML.)

Deming, W.E. 1993. The New Economics for Industry, Government, and Education. Cambridge, MA: MIT Center for Advanced Engineering Study.

Escobar, I.C. and A.A. Randall. 2001. Ozonation and distribution system biostability. J. AWWA, 93:10:77.

Friedman, M., G. Kirmeyer, J. Lemieux, M. LeChevallier, S. Seidl, J. Routt. 2010. Criteria for Optimized Distribution Systems. Denver: Water Research Foundation.

Hill, C.P., R. Giani, A.F. Cantor, E. Turner. 2011. Manual of Water Supply Practices – M58: Assessment and Control of Internal Corrosion and Metals Release in Drinking Water Systems. Denver: AWWA.

Hoffbuhr, J.W. (2006). 125 Years – Fullfilling a Mission. JAWWA 98:3:8 Lauer, B (1997). Optimization Begins But Never Ends. JAWWA 89:8:8 Luminultra. 2011. [online]. Available: <http://www.luminultra.com/Products-

Services/qga.html>. [Cited August 22, 2011]. Lytle, D.A. and M.R. Schock. 2005. Formation of Pb(IV) oxides in chlorinated water. JAWWA

97:11:102. Maynard, B. and D. Mast. 2006. Proceedings of the American Water Works Association 2006

Water Quality and Technology Conference. Denver: AWWA. McCluskey, C., Quinn, J. P., McGrath Source, J. W. 2005. An Evaluation of Three New-

Generation Tetrazolium Salts for the Measurement of Respiratory Activity in Activated Sludge Microorganisms. Microbial Ecology 49 (3), 379-387.

Partnership for Safe Water (PSW). 2011. [online]. Available: <http://www.awwa.org/Resources/PartnershipMain.cfm?ItemNumber=51227&navItemNumber=51231>. [Cited August 22, 2011].

Process Research Solutions, LLC (PRS). 2009. Investigation of Elevated Copper in Drinking Water. Engineering report. Madison, WI: Process Research Solutions, LLC.

Sandvig, A., P. Kwan, G. Kirmeyer, B. Maynard, D. Mast, R.R. Trussell, S. Trussell, A.F. Cantor, A. Prescott. 2008. Contribution of Service Line and Plumbing Fixtures to Lead and Copper Rule Compliance Issues. Denver: AWWA Research Foundation.

Schock, M.R. 2005. Distribution systems as reservoirs and reactors for inorganic contaminants. Distribution System Water Quality Challenges in the 21st Century – A Strategic Guide. Edited by M.J. McPhee. Denver: AWWA Research Foundation, 105-140.

Schock, M.R., R.N. Hyland, and M.M. Welch. 2008. Occurrence of contaminant accumulation in lead pipe scales from domestic drinking-water distribution systems. Environ. Sci. Technol. 42:12:4285.

Triantafyllidou, S. and M. A. Edwards. 2006. Effect of coagulant selection on lead leaching: Importance of the chloride to sulfate mass ratio. Proceedings of the American Water Works Association 2006 Water Quality and Technology Conference. Denver: AWWA.


169

Triantafyllidou, S. and M. Edwards. 2010. Contribution of Galvanic Corrosion to Lead in Water After Partial Lead Service Line Replacements. Denver: Water Research Foundation.

Tsukatani, T., Suenaga, H., Higuchi, T., Akao, T., Ishiyama, M., Ezoe, M., Matsumoto, K.. 2010. Colorimetric cell proliferation assay for Microorganisms in microtiter plate using water-soluble tetrazolium salts. Journal of Microbiological Methods 75, 109-116.

Van der Kooij, D. 1992. Assimilable organic carbon as an indicator of bacterial regrowth. J. AWWA 84:2:57.

Volk, C.J. and M.W. LeChevallier. 2000. Assessing biodegradable organic matter. J. AWWA 92:5:64.

Wheeler, D.J. and D.S. Chambers. 1992. Understanding Statistical Process Control, 2nd ed. Knoxville, TN: SPC Press.

Zhang, M., M.J. Semmens, D. Schuler, and R.M. Hozalski (2002). Biostability and microbiological quality in a chloraminated distribution system. J. AWWA 94:9:112.


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