SC IEN CE

United States

Environmental Protection

Agency

EPA/600/R-10/005 | March 2010 | www.epa.gov/ord

Detection of Biological 

Suspensions Using Online 

Detectors in a Drinking Water 

Distribution System Simulator

Office of Research and Development

National Homeland Security Research Center
ii

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EPA/600/R-10/005 | March 2010 | www.epa.gov/ord

Detection of Biological Suspensions 

Using Online Detectors in a Drinking 

Water Distribution System Simulator

United States Environmental Protection Agency

National Homeland Security Research Center

Cincinnati, OH 45268 

Office of Research and Development

National Homeland Security Research Center, Water Infrastructure Protection
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v

Table of Contents

Disclaimer .....................................................................................................................................................................vi

Executive Summary.....................................................................................................................................................ix

Introduction...................................................................................................................................................................1

Experimental Design.....................................................................................................................................................3

Single Pass Distribution System Simulator.............................................................................................................3

Water Quality Sensors .............................................................................................................................................3

Contaminants...........................................................................................................................................................3

Evaluation of Water Quality Sensor Response ........................................................................................................4

Results and Discussion..................................................................................................................................................5

Results of Sensor Response Experiments................................................................................................................5

Sievers® 900 On-Line Total Organic Carbon Analyzer and Hach CL17 Free Chlorine Analyzer..........................5

s::can Spectro::lyserTM and Hach FilterTrakTM 660 sc Laser Nephelometer...........................................................5

Real Tech Real UVT Online ....................................................................................................................................9

Fluid Imaging Technologies FlowCAM® ................................................................................................................9

JMAR Technologies BioSentry® .............................................................................................................................9

Discussion and Significance of Sensor Response Results.....................................................................................10

Sensor Cost Considerations...................................................................................................................................10

Conclusions and Future Work...................................................................................................................................13

References....................................................................................................................................................................15
vi

Disclaimer

The U.S. Environmental Protection Agency through its Office of Research and Development funded and collaborated in the 

research described herein under contract number EP-C-04-034 with Shaw Environmental and Infrastructure, Inc. It has been 

subject to an administrative review but does not necessarily reflect the views of the Agency. No official endorsement should be 

inferred. EPA does not endorse the purchase or sale of any commercial products or services.

Questions concerning this document or its application should be addressed to:

Jeffrey Szabo

National Homeland Security Research Center (NG-16)

Office of Research and Development

United States Environmental Protection Agency

26 W. Martin Luther King Dr.

Cincinnati, OH 45268

(513) 487-2823

szabo.jeff@epa.gov

or

John Hall

National Homeland Security Research Center (NG-16)

Office of Research and Development

United States Environmental Protection Agency

26 W. Martin Luther King Dr.

Cincinnati, OH 45268

(513) 487-2814

hall.john@epa.gov
vii

List of Tables and Figures

Table 1: Water quality sensors ........................................................................................................................................4

Table 2: Sensor response to contamination reported as absolute change (top), percent change (middle)

and signal-to-noise ratio (bottom).....................................................................................................................6

Table 3: Formulation for terrific broth and sporulation media........................................................................................9

Table 4: Sensor detection and purchase data ................................................................................................................11

Figure 1: Sensor baseline and response to contamination with Bacillus globigii spores at four inch pipes densities ...7

Figure 2: Sensor baseline and response to contamination with Escherichia coli K-12 at four inch pipes densities .....8

Figure 3: Multiple angle light scattering device response to Escherichia coli K-12 and Bacillus globigii .................10
viii

List of Acronyms

ATCC American Type Culture Collection

CWS Contamination Warning System

DPD N, N-diethyl-p-phenylenediamine

GAC Granular Activated Carbon

GCWW Greater Cincinnati Water Works

GPM Gallons per Minute

HSPD Homeland Security Presidential Directive

MALS Multiple Angle Light Scattering

MSD Metropolitan Sewer District

NTU Nephelometric Turbidity Unit

TOC Total Organic Carbon

US EPA United States Environmental Protection Agency

UV Ultraviolet
ix

Executive Summary

Detection of relatively low density microbial suspensions 

(less than 105 cfu/mL) was evaluated with a suite of online 

water quality sensors and instruments. Typically, the drinking 

water industry uses online sensors to measure parameters 

such as free chlorine, pH, and conductivity; however, in 

microbial suspensions below 105 cfu/mL, the sensors have a 

weak response or are ineffective. Therefore, sensors designed 

to detect either particulates in water or organic compounds 

that might accompany microbial suspensions (i.e., culture 

media) were evaluated for their ability to detect low density 

microbial suspensions.

Evaluation took place in a pilot scale drinking water 

distribution system simulator (DSS) with sensors attached 

through a slip-stream. Technologies investigated were 

the Fluid Imaging Technologies FlowCAM®, Hach 

FilterTrakTM 660 sc Laser Nephelometer, JMAR BioSentry®, 

Real Tech Inc., Real UVT Online and the S::CAN 

Spectro::lyserTM.

Biological suspensions were injected into the DSS and 

sensor responses were compared to stable baseline values 

before injection. The JMAR BioSentry® detected the least 

dense biological suspension (600 cfu/ml) while the S::CAN 

Spectro::lyserTM and Hach FilterTrakTM 660 sc Laser 

Nephelometer performed as well as the online free chlorine 

and TOC analyzers (2.5x104 cfu/ml). The results were 

determined by selecting the biological densities that elicited 

an obvious visual response from the sensor. However, it 

should be noted that changes not obvious to the naked eye 

could be detected with event detection algorithms. Operation 

and maintenance costs of the sensors are minimal, but 

some have high capital costs that must be considered when 

weighing their detection ability.
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1

Introduction

Detecting contamination in drinking water distribution 

systems has challenged water utilities for many years. Water 

utilities have traditionally focused on intrusion from incidents 

such as pipe breaks or plant malfunctions as the primary 

source of contamination, but in recent years utilities have also 

considered issues associated with intentional contamination. 

Contaminant-specific sensors show promise for detecting 

contamination, but their use is limited by factors such as cost, 

ease of use, commercial availability and limited acceptability 

by the drinking water community. Furthermore, the large 

number of contaminant-specific sensors needed to detect the 

universe of potential contaminants makes their use inefficient.

The drinking water community has explored the use of 

common water quality sensors to help detect contamination 

(Allmann et al., 2005; Byer et al., 2005; Kessler et al., 

1998; King et al., 2005; Kroll et al., 2005; Magnuson et al., 

2005). In response to Homeland Security Presidential 

Directives (HSPD) 7 and 9, the United States Environmental 

Protection Agency (USEPA) has undertaken research using 

a multifaceted contamination warning system (CWS) for 

drinking water distribution systems, of which online water 

quality monitoring is one component (USEPA, 2005a; 2006; 

2007; Hall et al., 2007; Szabo et al., 2006; HSPD 7, 2003; 

HSPD 9, 2004). Research efforts have remained focused on 

commercially available online water quality sensors, since 

they offer the dual benefits of water quality data and potential 

detection of contamination.

Detecting biological suspensions at low concentrations (less 

than 105 cfu/mL) in drinking water has proven challenging. 

Previous research has shown that biological suspensions, 

injected with the growth media in which they were cultured, 

will affect parameters like free chlorine and total organic 

carbon (TOC) in chlorinated water (Hall et al., 2007; 

USEPA, 2007). This is due to the nutrient media reacting with 

and reducing free chlorine and organic carbon in the broth, 

which increases TOC. If biological suspensions are washed 

and injected without growth media, or injected into a large 

volume where the growth media is highly diluted, standard 

water quality parameters show little noticeable response.

Therefore, it is sensible to study less common sensors to 

determine whether they can detect biological suspensions at 

lower densities than standard sensors. This paper describes 

detection studies with a suite of specialized water quality 

monitors and their response to biological contamination at 

1×102-2.5×104 cfu/ml. As-tested cost information is also 

included, which will hopefully provide perspective to any 

water utility or other user looking to employ the tested water 

quality monitors.
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3

Experimental Design

Single Pass Distribution System Simulator

The drinking water distribution system simulator used in this 

study was described in Yang et al. (2008). A drinking water 

distribution pipe was represented using a once-through (or 

single pass) pipe at USEPA’s Test and Evaluation (T&E) 

Facility in Cincinnati, Ohio. The pipe consisted of 1,200 feet 

of 3-inch diameter fiberglass-lined ductile iron. Experiments 

were conducted at 22 gallons per minute (gpm), which 

corresponds to an average velocity of 1 foot per second (ft/

sec) in the pipe. This flow rate will produce turbulent flow 

(Reynolds number approximately 26,000) in the relatively 

smooth pipe. Although the pipe was lined with fiberglass, 

sections have chipped away, exposing ductile iron. These 

sections were heavily corroded and were more representative 

of an iron drinking water pipe than the lined sections. Note 

that English standard units, commonly used by the U.S. water 

utility personnel, have been used throughout this report. For 

example, volume is reported in U.S. gallons and velocity 

in feet per second (ft/s). However, in keeping with industry 

usage, contaminant concentrations are reported in metric 

units, in milligrams per liter (mg/L).

Chlorinated tap water was introduced directly from the 

Greater Cincinnati Water Works (GCWW) distribution 

system into a 750 gallon storage tank where it was 

fed by gravity into the 3-inch pipe system. An air gap 

was maintained between the GCWW system and this 

experimental setup to ensure that there was no back flushing 

of the injected contaminant. Free chorine was generally 

1.0 +/- 0.1 mg/L, with temperature ranging from 10° to 30° C 

depending upon the season. Turbidity was 0.1 nephelometric 

turbity units (NTU) or less throughout the year. The water 

fed from the 750 gallon overhead tank provided a 10 to 12 

pounds per square inch (psi) inside the pipe. Contaminant 

injections were performed for 20 minutes by injecting a 

10 L mix of contaminant in chlorine-free granular activated 

carbon (GAC) filtered tap water at the rate of 0.5 L/min. 

Contaminant concentration in the pipe was varied by altering 

the amount of contaminant mixed in the 10 L volume. 

Control experiments were performed by injecting 10 L of the 

GAC filtered tap water without the contaminant at the same 

injection rate.

Water Quality Sensors

A suite of sensors measured water quality at 80 feet from the 

injection point through a slipstream. Past experimental results 

indicated that of the standard water quality parameters, 

free/total chlorine and TOC were the best at indicating 

contamination in chlorinated tap water (USEPA, 2006a; 

2005b). Total chlorine was measured using a Hach CL17 

Total Chlorine Analyzer (Hach Company, Loveland, Colo.), 

which uses the N, N-diethyl-p-phenylenediamine (DPD) 

colorimetric method (Standard Methods, 1998). TOC was 

measured using a Sievers® 900 On-Line Total Organic 

Carbon Analyzer (GE Analytical Instruments, Boulder, Colo.) 

The operation and maintenance of these sensors is described 

in Hall et al. (2007).

The following specialized water quality sensors were 

evaluated in this study to determine if their response was 

better than the standard TOC and chlorine sensors listed 

above:

• A laser turbidimeter, the Hach FilterTrakTM 660 sc 

Laser Nephelometer (Hach Company, Loveland, 

Colo.), was used as an enhanced turbidity monitor. It 

operates similarly to a standard turbidimeter, except that 

it employs a laser nephelometer, which yields better 

resolution.

• A BioSentry® Water Monitoring system (JMAR 

Technologies, San Diego, Calif.) represented a laser 

based multiple-angle light scattering (MALS) device. In 

addition to detecting changes in the number of particles 

in water, it has the ability to classify microorganisms 

using their unique bio-optical MALS signatures, but this 

information was not specifically analyzed in this study.

• Estimates of TOC and turbidity were obtained using 

a 100 mm s::can Spectro::lyserTM (s::can Meßtechnik 

GmbH, Vienna, Austria). The spectro::lyser operates 

using ultraviolet-visible (UV-Vis) absorption 

spectrometry in the 200 to 750 nm range.

• Continuous ultraviolet light transmission at UV 254 

nm wavelength (UV254) was made with a Real UVT 

Online monitor (Real Tech, Inc., Whitby, Ontario, 

Canada). Similarly to the spectro::lyser, changes in 

UV absorption by aromatics or other light absorbing 

compounds in the microbial suspension could be 

detected by this device.

• An online flow-cytometer and microscope called 

FlowCAM® (Fluid Imaging Technologies, Yarmouth, 

Maine) was used as a digital imaging microscope-based 

particle detector. Particles are channeled through a flow 

cell where they are digitally imaged and can be counted.

Contaminants

Two microorganisms were used in this study. Escherichia 

coli K-12 (ATCC 25204) (E. coli) was used as representative 

vegetative bacteria. Bacillus globigii (B. globigii) 

(obtained from the US Army’s Dugway Proving Ground, 

Dugway Proving Ground, Utah) was used in spore 

form and was considered to be a representative sporeforming bacterium. Storage and culturing methods 

are described in detail in Szabo et al. (2007) and Hall 

et al. (2007) for B. globigii and E. coli, respectively. 

Wastewater (secondary effluent) was also injected 

and was obtained from the Cincinnati Metropolitan 

Sewer District (MSD) Mill Creek treatment plant.
4

Table 1: Water quality sensors

Trademark or Brand Name Manufacturer’s Name, City and State Web Site

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

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

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

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

Real UVT Online Real Tech, Inc., Whitby, Ontario, Canada http://www.realtech.ca

Sievers® 900 On-Line Total Organic Carbon 

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

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

Evaluation of Water Quality Sensor Response

Sensor response to contamination was evaluated by 

calculating the absolute and percent change from a stable 

baseline to the peak value recorded as the contaminant passed 

the sensor. Baseline values were calculated by averaging 

the sensor signal over a one hour period before contaminant 

injection, with baseline noise represented by standard 

deviation. Sensors were polled every minute during test runs, 

so 60 pre-injection data points were used for determining 

baseline mean and standard deviation. Contamination 

injections were performed in duplicate, and results are 

presented as the average of those duplicates (see Table 2 in 

the results section).

Evaluating the data by calculating the percent change yields 

a good system specific response of water quality parameters 

to contaminants. However, percent change may be different 

in systems that have different water quality. For example, if 

a contaminant injected into water with 1 mg/L free chlorine 

decreases the chlorine concentration to 0.9 mg/L, a 10% 

reduction has occured. If the same contaminant injected at 

the same concentration in water with 2 mg/L free chlorine 

consumes the same amount of free chlorine, a 5% reduction 

has occured. Therefore, sensor response is also characterized 

as a signal-to-noise ratio. The maximum absolute change 

(baseline to peak) recorded during injection was normalized 

by the baseline standard deviation. Analyzing data with 

the signal-to-noise approach illustrates sensor response as 

the magnitude of the water quality change relative to the 

variation in the baseline before injection.

The time period when the injected contaminant was in 

contact with the sensors was determined based on the flow 

rate and injection duration. Injections were 20 minutes long 

and flow velocity was 1 ft/sec, so the injection reached the 

80 ft sensor station 1.3 minutes after injection and continued 

passing the sensors for 20 minutes. Sensor responses usually 

lasted longer than 20 minutes at this station due to dispersion, 

which elongated the contaminant plume in the pipe. Peak 

sensor responses were taken from the time periods when the 

contaminants were in contact with the sensors.

Although water quality sensors typically respond within 

seconds of water quality change, the Hach CL17 and 

Sievers® 900 On-Line Total Organic Carbon Analyzer used 

for this experiment run on cycles of 2.5 and 8 minutes, 

respectively. These instruments were polled every minute, 

but only returned new values at the end of their cycles. 

Still, new values were returned frequently enough that the 

changes in water quality were seen for both devices while the 

contaminant was passing the sampling point.
5

Results and Discussion

Results of Sensor Response Experiments

Table 2 summarizes the response of the standard 

and specialized sensors to various cell densities (or 

concentrations) of E. coli and B. globigii. Time series plots 

of some of the specialized sensor responses are presented 

for B. globigii and E. coli in Figures 1 and 2, respectively. 

These plots show the response of duplicate experiments 

in sequence, as well as the baseline data that precedes the 

injection.

Sievers® 900 On-Line Total Organic Carbon Analyzer 

and Hach CL17 Free Chlorine Analyzer

Total chlorine measured by the Hach CL17 decreased 

by 0.04 and 0.08 mg/L upon addition of B. globigii and 

E. coli, respectively, at 2.5x104 cfu/mL. The response of 

the JMAR Biosentry® was much larger at this cell density, 

but the chlorine response is comparable to the s::can and 

laser turbidimeter at 2.5x104 cfu/mL for both microbial 

suspensions. TOC measured by the Sievers® 900 changed by 

0.16 and 0.41 mg/L for B. globigii and E. coli, respectively, 

at 2.5x104 cfu/mL. The signal-to-noise ratio of 13.7 for 

E. coli at 103 may indicate a change, but no visual change 

was noticeable below this level. Sievers® 900 TOC and Hach 

CL17 Total Chlorine results confirm what has been reported 

in the past: there is a significant decrease in the response of 

both instruments when the cell density (or concentration) of 

injected microorganisms decreases below 2.5x104

 cfu/mL. 

It is important to note that the Sievers® 900 TOC and Hach 

CL17 Total Chlorine response occurs only when B. globigii

and E. coli are injected with their respective sporulation and 

growth media. If these growth media were washed away 

or sufficiently diluted, the Hach CL17 Total Chlorine and 

Sievers® 900 TOC sensors show little or no response. Signalto-noise values for the Sievers® 900 TOC analyzer are higher 

than the other instruments below 2.5x104 cfu/mL because 

TOC baseline was stable during these tests.

s::can Spectro::lyserTM and Hach FilterTrakTM 660 sc 

Laser Nephelometer

The response for the s::can spectro::lyser’sTM turbidity 

measurement channel is the same for each injected density 

and provides little indication that a contaminant is present. 

The percent change values are misleading since the average 

value of turbidity is low, but the data are noisy. When the 

peak during injection is compared to the mean, it appears 

that a large change has taken place. However, when signalto-noise is calculated, the response is one to two, indicating 

that the peak is close to the standard deviation in the baseline. 

Figures 1 and 2 confirm that visually distinguishing a 

turbidity response is difficult.
6

Table 2: Sensor response to contamination reported as absolute change (top), percent change (middle)

and signal-to-noise ratio (bottom)

Injected Agent

In-Pipe 

Concentration

(cfu/mL)

Hach Total 

Chlorine

Sievers 

®TOC

S::CAN TOC

S::CAN 

Turbidity

 Hach Laser 

nephelometer

JMAR 

Biosentry

®

FlowCAM

®

RealUVT

(mg/L) (mg/L) (mg/L) (FTU) (mNTU) (counts) (#/mL) (UVA)

E. coli

(in Terrific Broth)

1.0E+02

-0.01

-1.3%

-3.5

0.00

0.4%

1.0

0.01

0.5%

1.1

0.07

44%

1.4

1.7

6%

3.2

173

22%

9.4

No Data No Data

6.0E+02

0.00

0.0%

-0.1

0.01

1.0%

5.9 

0.01

0.8%

2.0

0.10

43%

2.3

2.9

12%

3.6

256

32%

17

No Data No Data

1.0E+03

-0.01

-0.9%

-4.2

 0.02

2.2%

13.7

0.01

1.1%

2.4

0.08

30%

1.8

1.7

6%

2.7

316

41%

16

446

230%

2.4

0.0003

1.7%

2.1

2.5E+04

-0.08

-7.0%

-19

0.41

36.1%

319.3

0.08

7.6%

16

0.09

65%

1.6

13

45%

21

4616

696%

263

2099

7591%

84

0.0002

1.4%

1.5

B. globigii

(in sporulation media)

1.0E+02

-0.01

-0.5%

-1.5

 0.01

0.9%

1.2

0.00

0.2%

0.4

0.10

86%

2.3

1.1

4.1%

2.0

86

12%

5.9

No Data No Data

6.0E+02

-0.01

-0.8%

-2.3

0.01

0.7%

1.2

0.01

0.5%

1.0

0.06

43%

1.5

0.9

3.3%

2.9

75

10%

5.6

No Data No Data

1.0E+03

0.00

0.1%

0.1

 0.01

1.1%

2.1

0.01

0.6%

1.2

0.08

63%

1.4

1.1

4.1%

1.9

123

17%

10.4

1.7

120%

1.6

0.0003

1.6%

1.9

2.5E+04

-0.04

2.9%

-9.6

 0.16

14.8%

42.0

0.03

2.6%

5.9

0.08

64%

1.7

7.6

27%

13.9

1706

255%

153

5.8

388%

5.6

0.0004

2.4%

2.4

Wastewater (Secondary 

Effluent)

10 L

(0.026 v/v)

-0.13

-11%

-32

0.07

6.4%

36.3 

0.07

6%

12

0.15

239%

3.1

34

116%

47

3356

538%

249

4079

1158%

9.5

0.0028

25.4%

13.2

Control Blank

(DI Water) 0

0.01

1.5%

3.6

0.01

1.9%

0.9

0.04

4.4%

17

0.09

15%

2.3

1.5

6.6%

3.3

29

3.8%

1.7

1907

4496%

40

0.0002

1.0%

1.4
7

Figure 1: Sensor baseline and response to contamination with Bacillus globigii spores at four inch pipe densities

B.globigii injected at 1x102 cfu/ml

Time (min)

0 100 200 300 400

JMAR (counts)

0

800600400200

1000

Laser Turbidimeter (mNTU) 5

0

3025201510

S::CAN TOC (mg/L)

1.21.00.80.60.40.20.0

S::CAN Turbidity (FTU)

0.50.40.30.20.10.0

JMARLaser TurbidimeterS::CAN TOC (mg/L)S::CAN Turbdiity (FTU)

B.globigii injected at 6x102 cfu/ml

Time (min)

0 50 100 150 200 250

JMAR (counts)

0

800600400200

1000

Laser Turbidimeter (mNTU)

50

353025201510

S::CAN TOC (mg/L)

1.21.00.80.60.40.20.0

S::CAN Turbidity (FTU)

0.50.40.30.20.10.0

JMARLaser TurbidimeterS::CAN TOC (mg/L)S::CAN Turbidity (FTU)

B.globigii injected at 1x103 cfu/ml

Time (min)

0 20 40 60 80 100 120 140 160 180 200

JMAR (counts)

0

800600400200

1000

Laser Turbidimeter (mNTU)

50

3025201510

S::CAN TOC (mg/L)

1.21.00.80.60.40.20.0

S::CAN Turbidity (FTU)

0.50.40.30.20.10.0

JMARLaser TurbidimeterS::CAN TOC (mg/L)S::CAN Turbidity (FTU)

B. globigii injected at 2.5x104 cfu/ml

Time (min)

0 50 100 150 200

JMAR (counts)

0

500

3000

2500

2000

1500

1000

Laser Turbidimeter (mNTU)

0

40302010

S::CAN TOC (mg/L)

1.00.80.60.40.20.0

S::CAN Turbidity (FTU)

1.00.80.60.40.20.0

JMARLaser TurbidimeterS::CAN TOCS::CAN Turbidity (FTU)
8

Figure 2: Sensor baseline and response to contamination with Escherichia coli K-12 at four inch pipe densities 

E. coli at 1x102 cfu/ml

Time (min)

0 50 100 150 200 250

JMAR Counts

0

800600400200

1000

Laser Turbidimeter (mNTU)

0

40302010

S::CAN TOC (mg/L)

1.61.41.21.00.80.60.40.20.0

S::CAN Turbidity (FTU)

1.00.80.60.40.20.0

JMARLaser TurbidimeterS::CAN TOCS::CAN Turbidity

E.coli at 6x102 cfu/ml

Time (min)

0 100 200 300 400

JMAR counts

0

800600400200

12001000

S::CAN TOC (mg/L)

1.21.00.80.60.40.20.0

S::CAN Turbidity (FTU)

1.00.80.60.40.20.0

JMARS::CAN TOCS::CAN Turbidity

Laser turbidimeter data is not valid for this trial. 

E.coli at 1x103 cfu/ml

Time (min)

0 50 100 150 200 250 300 350

JMAR Counts

0

800600400200

140012001000

Laser Turbidimeter (mNTU)

0

40302010

S::CAN TOC (mg/L)

1.21.00.80.60.40.20.0

S::CAN Turbidity (mg/L)

1.00.80.60.40.20.0

JMARLaser TurbidimeterS::CAN TOCS::CAN Turbidity

E.coli at 2.5x104 cfu/ml

Time (min)

0 50 100 150 200 250

JMAR (counts)

0

600050004000300020001000

S::CAN TOC (mg/L)

1.21.00.80.60.40.20.0

Laser Turbidimer (mNTU)

0

5040302010

S::CAN Turbidity (FTU)

1.00.80.60.40.20.0

JMAR Laser TurbidimeterS::CAN TOCS::CAN Turbidity
9

Table 3: Formulation for terrific broth and sporulation media

Terrific Broth (per liter of water) Sporulation Media (per liter of water)

Pancreatic Digest of Casein...12 g

Yeast Extract...24 g

Dipotassium Phosphate...9.4 g

Monopotassium Phosphate...2.2 g

Nutrient Broth...8 g

Manganese Sulfate...40 mg

Calcium Chloride...100 mg

s::can spectro::lyserTM TOC yielded a response for E. coli 

at 2.5x104 cfu/mL and a weak response for B. globigii at 

the same cell density. This is not surprising, since the s::can 

spectro::lyserTM detection principle is based on absorption 

of UV and visible light. Table 3 shows that the media in 

which the E. coli were cultured (Terrific Broth) is much 

more enriched in sugars and amino acids compared to the 

sporulation media in which the B. globigii spores were 

suspended. The higher concentration of compounds that 

absorb/emit light will lead to a greater response. The Hach 

FilterTrakTM 660 sc Laser Nephelometer responded in similar 

manner to the s::can spectro::lyserTM TOC, with a peak 

emerging during injection at the 2.5x104 cfu/mL density for 

E. coli and B. globigii.

Real Tech Real UVT Online

The Real UVT measures percent transmission (i.e., the 

light that is not absorbed) of UV light at 254 nm, so it was 

hoped that either light absorbing functional groups on the 

cells or the nutrient broth injected with the cells would elicit 

a change. The Real UVT showed little response to either 

E. coli, B. globigii or the control injection. This is likely 

due to either the functional groups in the nutrient broth or 

on the microorganism not absorbing light at 254 nm, or 

the functional groups were not concentrated enough. The 

wastewater injection caused a noticeable 25% (S/N 13.4) 

change from the baseline.

Fluid Imaging Technologies FlowCAM®

FlowCAM® response to the biological agents and secondary 

effluent was similar to the responses of an online turbidity 

sensor: percent changes were large, but signal-to-noise ratio 

was low due to high baseline variation. However, unlike the 

responses of a turbidity sensor, large baseline changes were 

sporadic and not always explainable. Large variability in the 

baseline made changes difficult to detect. The exception was 

the B. globigii injection at 2.5x104

 cfu/mL, where the signalto-noise ratio of 84 indicated a large, discernable change.

A good example of the FlowCAM® baseline variation 

is the control blank data where GAC filtered tap water 

elicited a change larger than some of the contaminants 

injections. The control blank should not cause a change 

larger than a contaminant injection. In later experiments, 

it was observed that touching or bumping the instrument 

during testing resulted in a spike in counts, which were 

likely due to the release of accumulated particles in the 

instrument plumbing. Also, flow to the optics for this 

device is controlled by a peristaltic pump, and this flow 

varied widely depending on how the length of the tubing 

in the pump. If flow is cut off from the optics, no detection 

will take place. Flow that is too fast might force particles 

through the flow cell too quickly to be counted. These 

instrument design/operational limitations could have led 

to the changes recorded during the control blank injection. 

The changes recorded by the FlowCAM® were likely real. 

Attributing changes to a contaminant – or other random 

event – proved difficult. Finally, images of the bacteria 

taken by the FlowCAM® were visible merely as small 

“dots.” The highest available resolution of the 20X objective 

was not enough to visualize the B. globigii or E. coli.

JMAR Technologies BioSentry®

Of the tested devices, the multiple angle light scattering 

device (JMAR Biosentry®) performed best, with an obvious 

response at 6x102 cfu/mL for E. coli and B. globigii. A 

change was not visible at the 1×102 cfu/mL level due to 

the noise in the baseline. Figure 3 shows the change in 

counts from baseline plotted against the increasing density 

of both biological agents. The output from the BioSentry®

is in “unknown counts,” or the number of particles that the 

machine is counting. At any concentration, the response is 

larger for E. coli compared to B. globigii since the vegetative 

cells are larger and easier to detect than the spores. 
10

Figure 3: Multiple angle light scattering device response to Escherichia coli K-12 and Bacillus globigii

y = 0 .0 6 5 9 x + 5 7 .2 9 6

R 2

 = 0 .9 9 9 5

y = 0 .1 7 8 7 x + 1 4 6 .9 4

R 2

 = 1

1 .E +0 0

1 .E +0 1

1 .E +0 2

1 .E +0 3

1 .E +0 4

1 .E +0 0 1 .E +0 1 1 .E +0 2 1 .E +0 3 1 .E +0 4 1 .E +0 5

Initia l Conc e ntra tion (c fu/m l)

U nk nown C ount C ha nge

B . g lo b ig ii

E . co li

Discussion and Significance of Sensor Response Results

B. globigii and E. coli were visibly detected at

2.5x104 cfu/mL by all of the sensors tested. At the 1,000 

and 600 cfu/mL cell densities, the Biosentry® provided 

the only visually significant response. None of the sensors 

provided any significant visual response at the 100 cfu/mL 

cell density. It should also be noted that 10 L of secondary 

effluent triggered an easily detectable response from each 

sensor used in this study due to the fact that it contains 

large particles and has a relatively high organic content 

compared to tap water. This is particularly interesting when 

considering that wastewater cross connections in drinking 

water distribution systems may be detected by standard and 

specialized sensors since raw effluent would presumably 

be more concentrated in solids, organics and nutrients.

The sensor response results have been presented as what 

can be visually detected as a change from a baseline. 

Visual changes can be useful when manually evaluating 

sensor data, but whether someone can discern a change 

depends on the variability of the baseline data. The 

experimental conditions in this study were ideal since 

baseline variation was minimal, but variability can increase 

in the field. Therefore, event detection algorithms should 

be considered for use in conjunction with standard or 

specialized water quality sensors to lower detection levels 

(McKenna et al., 2008). Although the human eye may not 

be able to discern a change from baseline data, an event 

detection algorithm may detect it. However, the data 

provides a glimpse of the water quality changes that could 

be caused by biological contamination. Even if an event 

detection algorithm indicates an unusual change, the data 

that caused the alarm will likely be manually examined.

Finally, adding a specialized sensor could benefit a 

chloraminated drinking water system. Inactivation of 

microorganisms will be slower in chloraminated water, 

especially Bacillus spores, so the results presented for 

chlorinated water should transfer to chloraminated 

systems. Furthermore, including devices like the s::can 

spectro::lyserTM or Sievers® 900 will add TOC as an online 

monitoring parameter in an online water quality monitoring 

station. This is especially important for chloraminated 

systems since total chlorine is not as effective at responding 

to contamination in chlorinated water as in chlorinated water 

(Szabo et al., 2008).

Sensor Cost Considerations

Table 4 lists the costs of each piece of equipment. The cost of 

the JMAR BioSentry® unit used in these tests was $46,215. 

Should there be a particularly sensitive point in a utility’s 

distribution system the BioSentry® may be an effective tool 

for detecting low level biological contamination. However, 

should a utility wish to deploy more units, they may look 

to standard online sensors that cost less, but cannot detect 

biological suspensions as low as the BioSentry®. If biological 

contamination is a concern to a water utility, then this 

tradeoff between detection and cost must be considered.
11

Table 4: Sensor detection and purchase data

Sensor Density at which sensor responded 

(cfu/mL)

Approximate Purchase Price 

(year 2007 $)

JMAR BioSentry® 2.5x104, 1x103, 6x102 50,000

s::can spectro::lyserTM TOC 2.5x104

25,000 Spectro::lyserTM turbidity 2.5x104

Hach FilterTrakTM 660 sc Laser Nephelometer 2.5x104 5,000

RealTech Real UVT – 5,000

FlowCAM® 2.5x104 35,000

Sievers® 900 On-Line Total Organic Carbon Analyzer 2.5x104 25,000

Hach CL17 Total Chlorine Analyzer 2.5x104 5,000
12

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13

Conclusions and Future Work

The data from the studies indicate that some specialized 

sensors can detect biological suspensions at lower densities 

in drinking water than standard online water quality sensors. 

The JMAR BioSentry®, which uses multiple angle light 

scattering (MALS), detected the lowest concentration 

(600 cfu/mL), while the s::can spectro::lyserTM TOC and 

Hach FilterTrakTM 660 sc Laser Nephelometer performed as 

well as the Hach CL17 Total Chlorine Analyzer and Sievers®

900 On-Line Total Organic Carbon Analyzer. An important 

component to any future work would be using an event 

detection algorithm in concert with the online water quality 

sensors. Even though an obvious visual change did not occur 

at low levels of contamination (100 to1,000 cfu/mL) for all 

sensors, there may be a subtle change that an algorithm could 

detect that the human eye cannot. This is especially important 

if a specific water quality parameter is “noisy” at the location 

it is being monitored. Other conclusions are as follows:

• The operational and maintenance costs of all the 

specialized optical devices tested were favorable. 

There are no reagents to buy and replace and no major 

maintenance issues were observed during the testing. 

• As with all online turbidity and particle counting sensor 

equipment, control of bubble formation is needed to 

prevent false alarms.

• There is a wide range of capital costs for the equipment 

tested. This is good news for potential consumers of this 

equipment, since multiple sensors of varying cost can be 

used together to create a more comprehensive detection 

network. Furthermore, it is anticipated that the cost of 

the multiple angle light scatter device can be reduced as 

larger market demand is generated and cost efficiencies 

are identified.

• The lower detection capability of the more expensive 

equipment should be weighed against the higher cost 

of the equipment. For example, one expensive device 

with lower detection capability could be deployed to 

a sensitive area while multiple less expensive devices 

could be more widely distributed.

Finally, although some of these devices did not respond to 

injections of microbial suspension, it should be noted that the 

manufacturers might not have designed them for detection of 

low density biological suspensions. Results from this study 

should not be used to evaluate the detection capabilities 

of these devices when used in other scenarios or for their 

intended purpose.
14

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15

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