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SAFEWATER – Application and Results of Innovative Tools for the Detection and Mitigation of CBRN- related Contamination Events in Drinking Water Supply Systems

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The security of drinking water is increasingly recognized as a major challenge for municipalities and water utilities. The safety and or security of drinking water can be threatened by natural disasters, accidents or malevolent attacks. The European FP7 project SAFEWATER (10/2013 - 12/2016) developed a comprehensive event detection and event management solution for drinking water security management and mitigation against major deliberate, accidental or natural CBRN related contaminations. The aim of this paper is to present the main results of the SAFEWATER project with a focus on (1) new sensors for detection of chemical, biological and radiological threats, (2) Event Detection System and (3) results of real-life user-case scenarios which have been investigated at three water utilities.
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CCWI 2017 – Computing and Control for the Water Industry Sheffield 5th - 7th September 2017
SAFEWATER – Application and Results of Innovative Tools for the
Detection and Mitigation of CBRN- related Contamination Events in
Drinking Water Supply Systems
Thomas Bernard,1 Aharon Rosenberg2, Helena Lucas3, Adrian Rieder4, Jürgen Moßgraber1,
Jochen Deuerlein5, Eyal Brill6, Karim Boudergui7, Dag Ilver8, Nirit Ulitzur9, Anna Elinge
Madar10
1Fraunhofer IOSB, Karlsruhe, Germany;
2Hagihon Ltd., Jerusalem, Israel
3Aguas do Algarve SA, Faro, Portugal
4Wasserversorgung Zürich, Zürich, Switzerland
53S Consult GmbH, Karlsruhe, Germany
6Decision Makers Ltd., Shoam, Israel
7CEA-LIST, Gif-sur-Yvette, France
8ACREO AB, Kista, Sweden
9BioMonitech Ltd., Qiryat Tivon, Israel
10ARTTIC, Paris, France
1thomas.bernard@iosb.fraunhofer.de, 2Aharon.Rosenberg@hagihon.co.il,
3h.lucas@aguasdoalgarve.pt, 4adrian.rieder@zuerich.ch, 5deuerlein@3sconsult.de,
6karim.boudergui@cea.fr, 7Dag.Ilver@acreo.se, 8admin@decisionmakers.biz,
9
nirit@biomonitech.com , 10
ellinge@arttic.eu
ABSTRACT
The security of drinking water is increasingly recognized as a major challenge for municipalities
and water utilities. The safety and or security of drinking water can be threatened by natural
disasters, accidents or malevolent attacks. The European FP7 project SAFEWATER (10/2013 -
12/2016) developed a comprehensive event detection and event management solution for drinking
water security management and mitigation against major deliberate, accidental or natural CBRN
related contaminations. The aim of this paper is to present the main results of the SAFEWATER
project with a focus on (1) new sensors for detection of chemical, biological and radiological
threats, (2) Event Detection System and (3) results of real-life user-case scenarios which have
been investigated at three water utilities.
Keywords: drinking water networks, CBRN sensors, real-time event detection, event management,
online simulation
1 BACKGROUND
The security of drinking water is increasingly recognized as a major challenge for municipalities
and water utilities. The safety and/or security of drinking water can be threatened by natural
disasters, accidents or malevolent attacks. In the event of a contamination, the contaminant will
often spread in the water system rapidly and extensively before the problem is detected.
Contaminated drinking water can lead to fatalities, induce major epidemics, disrupt economic life
and create mass panic. A first generation of software packages and sensors have been developed
prior to SAFEWATER for managing drinking water safety and security and in particular to detect
incidents. However these first generation tools suffer from a range of serious shortcomings: (1) the
set of available CBRN sensors, which are capable of detecting contamination threats to water
CCWI 2017 – Computing and Control for the Water Industry Sheffield 5th - 7th September 2017
drinking quality, is very limited; (2) real-time detection and alarm capabilities are non-existing or
insufficient; (3) offline and online simulators are currently difficult to use for crisis management as
the simulators are not integrated in a holistic platform and corresponding workflows; (4) online
simulators for response, mitigation and recovery are almost nonexistent for real world systems at
present; (5) there is currently no Event Management System available on the market which provides
a user interface for the decision makers, which connects all software components and which
provides all relevant information in a web based geographical information system (GIS).
The European FP7 project SAFEWATER [1] developed a comprehensive event detection and event
management solution for drinking water security management and mitigation against major
deliberate, accidental or natural CBRN related contaminations. provides an overview of the
structure of the SAFEWATER system. The key module is the Event Management System (EMS)
which handles incoming events and provides decision support in case of a crisis (as well as for
routine operations). The Event Detection System (EDS) breaks ground by detecting potentially
dangerous constellations of water quality parameters. These constellations may indicate a
contamination of the drinking water network, or a so-far unknown operational effect. In case of an
event it is important to quickly provide decision support regarding the best mitigation measures
(e.g. opening/closing of valves). In case of an event, online response tools can predict the spread of
the contamination and calculate optimal measures to minimize the impact of the contamination. The
simulators can also be used in an offline context in order to train the operational staff. Furthermore,
the simulators are used in order to train the event detection system. Within the SAFEWATER
project, enhanced CBRN sensors are also developed which provide the ability for an early detection
of CBRN contaminations. The aim of this paper is to present the main results of the SAFEWATER
project with a focus on (1) new CBRN sensors for detection of chemical, biological and radiological
threats, (2) Event Detection System and (3) results of real-life user-case scenarios which have been
investigated at three water utilities. For details regarding the Event Management System (EMS) see
[3]. Concept and results of the offline and online simulation tools are described in [4, 5].
Figure 1:
Structure of the
SAFEWATER system
CCWI 2017 – Computing and Control for the Water Industry Sheffield 5th - 7th September 2017
2 New CBRN Sensors
2.1 Sensor for Detection of E.-Coli
One common biological threat to drinking water is sewage contamination. Escherichia coli (E.coli)
is used as a universal indicator bacterium for sewage since it is found in human and mammalian
intestine and faeces and its presence in drinking water indicates contact of the water with sewage.
In the SAFEWATER project, Acreo has developed an autonomous system for detection of E. coli in
drinking water. This system is based on antibody based fluorescent labeling of E. coli bacteria,
followed by optical detection of E. coli using a video based flow cytometer. The instrument takes in
a water sample and mixes it with the fluorescent labeling agent and incubates it for 10 minutes at
39-40 °C to allow the fluorescent antibodies to bind to the surface of E. coli bacteria before the
sample is pumped through the optical sensor that records the now fluorescent bacteria. The optical
data is collected by a video camera, and after signal processing the approximate number of
bacteria/ml is established.
The instrument (Figure 2, left) can work autonomously, making measurements up to two times per
hour for weeks, until it is necessary to refill reagents. The results are transferred via regular SCADA
systems (4-20 mA signal, ModBus or GSM).
Figure 2: E. coli sensor (left) and the functional parts of the E. Coli sensor (right)
Sewage concentration
tap
water
Figure 3: Counted E. coli bacteria.
Demonstration of dose response
when increasing amount of sewage
was added to tap water. The first
column from left is pure tap water,
followed by increasing mix in of
pre-sedimented sewage.
CCWI 2017 – Computing and Control for the Water Industry Sheffield 5th - 7th September 2017
The sensor has been tested at water facilities in Jerusalem (HaGihon), and in Zurich (WVZ).
Examplary results are shown in Figure 2Error: Reference source not found. Here, either pure tap
water, or tap water contaminated with small amounts of pre-sedimented sewage has been analyzed.
As we can see, the pure tap water gives a low background signal, but increasing amount of sewage
gives a proportionally increasing signal. Thus, it is possible to use the sensor as an early warning
system. By using labeling agents with another specicity, this system can also be used for detection
of bacteria other than E. coli.
2.2 Toxicity Sensor based on Bioluminescence
Commonly used chemical analysers can detect and identify a restricted number of substances.
Numerous other contaminants may occur, that cannot be detected and/or that may have unknown
toxic properties (there are more than 87,000 chemicals used in the USA). This is why biological
early warning systems such as those utilizing luminescent bacteria offer the potential to monitor a
truly wide spectrum of dangerous contaminants, even those that escape conventional analytical
monitoring. Currently, there are several players in this market, e.g. MicroLan (The Netherlands);
Modern Water (UK); Applitek (Belgium). All these sensor systems are based on the same bioassay-
utilizing Vibrio fischeri and sodium chloride solution. This method has limited sensitivity when
used to test drinking water quality.
Biomonitech’s has developed an online sensor (BMT200) utilizing luminescent bacteria as
sensitive sensors for drinking water toxicity. Changes in light level reflect degree of toxicity. The
sensor utilizes a technology that does not look for specific contaminants; it is therefore effective
also for detecting chemicals that are not expected to be found in the water. The bacteria are
sensitized to low concentrations of a broad rage of chemical toxicants by using proprietary assay
buffers: One that sensitizes the bacteria to cationic heavy metals and metalloids, the other to
(mainly) organic toxicants, see Error: Reference source not found (right) for example of
discriminatory response. The development process and design committed to reducing the
manufacturing costs, minimizing moving parts, stabilizing the biotechnological core (the bacteria’s
light level under weeks-long continuous operating conditions). The sensor has been tested at water
utility HaGihon (Figure 1, left).
CCWI 2017 – Computing and Control for the Water Industry Sheffield 5th - 7th September 2017
Figure 4: Left: Sensor prototype version deployed in HaGihon (Jerusalem), right: Response to
various concentrations of the organic herbicide 2,4-D (Xppm, 2Xppm, 3Xppm – marked with red
arrows; spiked into local Israeli mineral water) in the two assay chambers
BMT200 continuously monitors the presence of chemical contamination in drinking water.
Concentrations of contaminants are reported within 15 minutes (either over a web frontend or via
serial ModBus linked to SCADA). BMT200 connects through a bypass to an inlet water line,
continuously drawing water samples at a rate of four per hour. Once toxicity is detected, the
suspected sample is drained into a “grab sample container” for further off-line analysis. The sensor
adapts to the local water quality of the site in which it is installed, and is not sensitive to normal
seasonal and spatial water quality variations. The sensor is fully automatic, requiring attention once
per month to refill reagents that feed the core biotechnology of the sensor.
2.3 Radioactivity Sensor
To manage the challenge of detecting alpha and beta radiation, CEA (France) has designed a new
plastic scintillator light collection system and developed associated high velocity electronics and
data processing algorithms. This new approach uses scintillating optical fibres as scintillator and
optical guide, to come close to the bulk of the water, in order to increase the detection area. The
scintillating fibres convert the radiation into light that travels through each optical fibre and is
finally detected by the PMT (Photon Multiplier Tube). To do a measurement in water and online,
the system needs to have a high sensitivity due to the small free path of particles (for alpha particles
with an energy of 5 MeV, the free path is 37.2µm; for beta particles with an energy from 50keV to
2 MeV, the free path is contained between 43µm and 1 cm). The beta radiation sensitivity is linked
to the active measurement volume and the alpha sensitivity is linked to the detector surface. So the
sensitivity is globally linked to the number of scintillating optical fibres.
The system is composed of a sensor part (Error: Reference source not found) including a
scintillating optical fibre bundle with its PMT (Photon Multiplier Tube) and an acquisition /
processing unit including a display and an embedded computer. Error: Reference source not found
shows the linearity of the detector response to detect alpha particle online. The sensitivity of the
system could be enhanced by increasing the number of optical fibres in the system (increasing the
active measurement volume).
CCWI 2017 – Computing and Control for the Water Industry Sheffield 5th - 7th September 2017
Figure 5:
Radioactivity
Sensor (CEA) Figure 6: System response for alpha contamination
3 Event Detection System
The Event Detection System (EDS) is a term coined by the EPA (Environmental Protection
Agency, U.S.) for a software system which can detect a water quality event before it turns into a
critical problem. A typical EDS system is based on non-supervised machine learning algorithms.
The need to practice a non-supervised algorithm comes from the nature of water distribution
systems. In such systems, True Events which involve acidents or deliberate man-made injection of
contamination into the water are rare. Hence the need to learn what constitutes the common/normal
situation and thus be able to detect the abnormal situations based on the knowledge of what is
normal.
The EDS which has been developed by Decision Makers Ltd. is based on several algorithms. Each
algorithm is called a Detector and is designed to perform a specific task. Detectors are grouped into
four basic groups:
Group 1 : Single sensor algorithms. Algorithms which learn the statistical limit, trend and rate
of change of each variable separately. A violation of each of the learned limits generates an
alarm after an appropriate delay time.
Group 2: Multi sensor algorithms . Automatic building of density functions of past
combinations of sensor values. Using the density function, the EDS can detect rare
combinations; also learning of the dynamics of value combinations is possible.
Group 3: Rule-violation algorithms . Procedure for testing sensor values with a set of predefined
engineering rules defined by the user.
Group 4: Hazard similarity detection . These detectors identify when the water quality is
approaching a combination that was classified in the past as a hazardous combination.
Each detector has a severity level and a delay time. When even a single detector triggers an alarm,
an event is generated. An event may include several alarms. Events can be delegated to operators
using email or SMS. They are also displayed in the SAFEWATER Event Management System.
CCWI 2017 – Computing and Control for the Water Industry Sheffield 5th - 7th September 2017
Once an event has been dealt with, it should be classified by an authorized operator. Classification
may be to one of the following: True event, False Event, Ignore (not to be used as a learning event)
or Other. True event means that the operator confirms that triggering the alarm was justified. In the
case of True Positive classification, detectors which triggered alarms are rewarded “Good points”.
False Event means that the operator rejects the decision of the EDS to trigger an alarm. Detectors
which trigger such alarms are penalized with “Bad points”. Ignore and Other classifications do not
grant points to detectors. Ignore is used to classify non-important events (e.g. some test). Other is
used to document events which are not used for the learning process but are important from
managerial point of view. Over time, detectors with “Bad points” should be calibrated. Thresholds
should be increased and/or delay time should be extended, until the specific detector stops gaining
bad points.
The EDS has been implemented, tested and evaluated at all three SAFEWATER water utilities
(HaGihon - Jerusalem, WVZ – Zurich and AdA - Portugal) [1].
4 Use Cases
Use Cases in the SafeWater project were designed to guide the SAFEWATER solution by setting
up a number of real-life scenarios of contamination events likely to challenge municipal water
supply systems. The use case scenarios were later used to test the effectiveness of the
SAFEWATER solution components in dealing effectively with the use cases.
4.1 Detection of Contaminations at Water Utility HaGihon
One of the use cases of water utility HaGihon (Jerusalem) was defined as follows: A major
municipal water line operating under pressure and supplying a large section of a city is
contaminated, as a result of a terror attack by pumping a water solution contaminated with a
mixture of CBRN contaminants into the supply line
Aspects of this use case were tested at the Beta-Site facility as well as in a large water supply
demonstration area (DMA) within HaGihon's distribution system. The chemical, biological and
radiation sensors were all installed in the Beta-Site and tested by running appropriate contaminants
through the system and ascertaining the ability of the sensors to detect the contaminant on-line and
to generate an immediate alarm. (Radioactive materials were used only at the CEA facility).
HaGihon also installed several Low Energy (LE) water quality sensors in the DMA and ran
simulation tests to establish the sensors' ability to detect abnormal turbidity and generate an alarm.
The project included optimization of the number and location of LE sensors in the DMA, carried
out by 3S Consult.
CCWI 2017 – Computing and Control for the Water Industry Sheffield 5th - 7th September 2017
A vital part of the SAFEWATER solution is the on-line hydraulic "look ahead" simulator
developed by 3S Consult and Fraunhofer IOSB. This advanced tool allows an event manager to
quickly examine how far and where a contaminant has dispersed in the distribution system, thus
allowing the operator to take steps to halt the spread in near-real-time. HaGihon examined the
simulator by running 6 contamination scenarios within the above DMA and ascertaining that the
model could predict the contaminant dispersion (see exemplary result in Error: Reference source
not found). Advanced features of the model include the capability to locate the possible point of
contamination by examining the time-of-alarm produced by several sensors in the distribution
system, as well as indicate which valves should be closed in the water distribution system in order
to effectively halt the contaminant spread at a given time-after-introduction.
Another vital part of the SAFEWATER solution is the Event Mangement System (EMS) that
incorporates a user-prompting menu for dealing with contamination events, including emphasis on
the Public Relations aspect of such an event. Keeping in mind that Public Relations is at least as
important as the engineering aspects of managing a contamination event, the EMS prompts the
Event Manger to initiate press conferences, update key stakeholders, etc.
4.2 Test Network and Investigations at Water Utility Zurich
The test network located on the water utility WVZ premises in Zurich consists of 150 m pipe length
in total and is 5 m wide (Figure 8 left). The test network has two long pipes and four different types
of junctions to simulate a real water distribution network with several distinct flow conditions. A
pump is installed to dose a contamination within a range of 100 to 12’000 mL/h. As general
detection system, five multi parameter probes from Intellitect™ are installed, which can be inserted
at any of the available quality measurement locations (“QM” in Figure 8 right).
Figure 7: Map produced by the
hydraulic dissemination model of a
contamination event in HaGihon
DMA. The dissemination of the
contaminant according to
concentrations is shown as different
colours
CCWI 2017 – Computing and Control for the Water Industry Sheffield 5th - 7th September 2017
Figure 8: Left: Picture of the test network with indicated contanmination and clean water branch.
Right: Setup of the test network during the contamination experiment. The blue dashed line shows
the flow path of the clean reference water in the upper part of the test network while the red dashed
line shows the flow path of the contaminated water in the bottom part. The blue circle around F2
indicates the second input location for drinking water.
A realistic contamination scenario with different concentrations of sewage contamination was
performed at the test network in order to evaluate the detectability of a sewage contamination in a
drinking water network. Actual pre-treated waste water from the nearby waste water treatment plant
was added to the drinking water of the main branch in the test network. A second branch of the test
network, fed by clean tap water, allows to distinguish between fluctuations of the parameters due to
changes in the tap water compared to changes due to contamination. Prefiltered sewage was
injected close to the inlet to reach three different concentrations of sewage in the system: 0.1%,
0.3%, and 0.5%. Multiparameter probes measuring pH, oxidation-reduction potential (ORP),
conductivity, dissolved oxygen (DO) and temperature. Water particle counter measuring particle
down to a size of 1 µm. Spectrolyser measuring the adsorbance at 256 different wavelengths
ranging from 190 to 720 nm. Online bacterial analyser measuring the total cell count (TCC) and
high/low nucleic acid content of each individual cell. The resluts clearly show that some of the
classical parameters, like pH, ORP and conductivity are unsuitable to detect sewage contamination,
while the particle values of the contaminated branch change by up to 6000% (!) in comparison to
the uncontaminated tap water. Most of the parameters measured by the Intellitect probe either show
no or insignificant differences between the contaminated and uncontaminated branch (maximum
4% difference). This is the case for all except the dissolved oxygen content, which has a difference
of up to 12%. It needs to be mentioned that the amount of dissolved oxygen in the distribution
network of WVZ fluctuates depending on the water source. The groundwater has an oxygen content
of approximately 10 mg/L. In comparison, the treated lake water has a higher oxygen content of
about 15 mg/L because of its double treatment with ozone. Therefore, the amount of dissolved
oxygen in drinking water from Zurich can naturally fluctuate by up to 30%. The S::CAN
spectrolyser with its adsorbance measured at 256 wavelengths can clearly detect the addition of
sewage down to 0.1%. The online bacteria analyser (OBA) measures a difference of up to 1400%
for TCC and 7000% for HNA between tap water and contaminated tap water.
5 CONCLUSIONS
CCWI 2017 – Computing and Control for the Water Industry Sheffield 5th - 7th September 2017
Within this paper we have presented the main results of the SAFEWATER project with a focus on
(1) new sensors for detection of chemical, biological and radiological threats, (2) Event Detection
System and (3) results of real-life user-case scenarios which have been investigated at three water
utilities (HaGihon - Jerusalem, WVZ – Zurich and AdA - Portugal). The features and functionality
of the SAFEWATER solution has been demonstrated in a live demo at a test network of water
utility WVZ (Zurich) in 11/2016. In the future it is planned to install and enhance the
SAFEWATER solution at further water utilities.
Acknowledgements
This project has received funding from the European Union's Seventh Framework Programme for
research, technological development and demonstration under grant agreement no. 312764.
References
[1] http://safewater-project.eu/ ; https://youtu.be/Bs5SljKUxgE (access on 28.06.2017)
[2] Bernard, T.; Moßgraber, J.; Elinge Madar, A.; Rosenberg, A.; Deuerlein, J.; Lucas, H.;
Boudergui, K.; Ilver, D.; Brill, E.; Ulitzur, N.: „SAFEWATER - Innovative tools for the
detection and mitigation of CBRN related contamination events of drinking water.“ 13th
International Conference on Computing and Control for the Water Industry (CCWI) 2015,
Leicester (UK). Procedia Engineering 119 (2015), S.352-359. ISSN: 1877-7058. DOI:
10.1016/j.proeng.2015.08.895
[3] E. Santamaria, J. Moßgraber, J. Deuerlein, C. Kühnert, T. Bernard: "An integrated system
for enhanced response management in CBRN related contamination events of drinking
water". In: Proc. 14th International Conference on Computing and Control for the Water
Industry (CCWI) 2016, Amsterdam (Netherlands). 7.-9.11.2016.
[4] T. Bernard, C. Kühnert, E. Santamaria, J. Moßgraber, J. Deuerlein, N. Guth, L. Meyer-
Harries, L. Ovadia, A. Rosenberg: "Online Hydraulic and Water Quality Simulator as a
Tool for Contamination Event Response". In: Proc. 14th International Conference on
Computing and Control for the Water Industry (CCWI) 2016, Amsterdam (Netherlands). 7.-
9.11.2016.
[5] J. Deuerlein, L. Meyer-Harries, N. Guth: "Efficient Online Source Identification Algorithm
for Integration within Contamination Event Management System". In: Proccedings of
International Conference on Computing and Control for the Water Industry (CCWI) 2016,
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The safety and or security of drinking water can be threatened by natural disasters, accidents or malevolent attacks. The European FP7 project SAFEWATER aims at developing a comprehensive event detection and event management solution for drinking water security management and mitigation against major deliberate, accidental or natural CBRN related contaminations. New cost-effective C, B, and RN sensors will be developed. An innovative concept with a broad network of low-cost sensors – “domestic sensors” (complementary to a set of sensors in strategic locations) will be developed. A technology platform will be provide which is able to capture and analyze the data collected by the sensors and from other information systems and give a full overview of the crisis to the responders by means of online look-ahead simulations to efficiently manage potential crises. For testing the SAFEWATER solution it will be integrated with on utility-partners’ information systems.
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The security of drinking water is increasingly recognized as a major challenge for municipalities and water utilities. The safety and or security of drinking water can be threatened by natural disasters, accidents or malevolent attacks. In the event of a contamination water spreads rapidly and hence extensively before the problem is detected. Contaminated drinking water can induce major epidemics, disrupt economic life and create mass panic. A first generation of software packages and sensors has been developed for managing drinking water safety and security and in particular for detecting incidents, such as the Guardian Blue from Hach Lange, Canary from EPA. They allow for an overall management of water security including the systematic collection and interpretation of information by online sensors. However this first generation of tools suffers from a range of serious shortcomings: (1) Real-time detection and alarm capabilities are non-existing or insufficient; (2) current limitations of propagation models make the effective situational assessment of potentially contaminated zones very difficult; (3) so far no generic approach for the online-calibration of the hydraulic and transport model exists; (4) Models for response, mitigation and recovery that are almost inexistent for real world systems at present; (5) the set of available CBRN sensors, which can be used to detect contamination threats to water drinking quality, is very limited. The European FP7 project SAFEWATER (10/2013 - 12/2016) aims at developing a comprehensive event detection and event management solution for drinking water security management and mitigation against major deliberate, accidental or natural CBRN related contaminations.
Efficient Online Source Identification Algorithm for Integration within Contamination Event Management System
  • J Deuerlein
  • L Meyer-Harries
  • N Guth
J. Deuerlein, L. Meyer-Harries, N. Guth: "Efficient Online Source Identification Algorithm for Integration within Contamination Event Management System". In: Proccedings of International Conference on Computing and Control for the Water Industry (CCWI) 2016, Amsterdam (Netherlands). 7.-9.11.2016.