SAFEWATER – Application and Results of Innovative Tools for the Detection and M itigation of CBRN-related Contamination Events in Drinking Water Supply Systems Thomas Bernard, Aharon Rosenberg, Helena Lucas, Adrian Rieder, J ürgen M oßgraber, J ochen Deuerlein, Eyal Brill, Karim Boudergui, Dag Ilver, Nirit Ulitzur, Anna Elinge M adar Conference CCWI 2017 Sheffield, 5.-7.9.2017 1
Information we need when a CBRN event contaminates the drinking water supply… 2
Water security is compromised by deliberate or accidental contamination 3
Accidental Contaminations 11.09.2015
At project start (10/ 2013) … No end to end holistic solution available for the detection and management of a CBRN water safety event 6
At project start … No online sensors for microbial or radiological detection Only a few sensors for specific chemicals‘ detection No reliable tools for real time detection/ online simulation 7
SAFEWATER Objectives Global generic solution for detection and mitigation of drinking water event resulting from CBRN contamination 8
SAFEWATER Objectives New CBRN water quality sensors Improved contamination alert systems Simulators to determine source and spread M anagement system to provide decision support Testing the system in three water utilities in six use cases 9
SAFEWATER Objectives New CBRN water quality sensors Improved contamination alert systems Simulators to determine source and spread M anagement system to provide decision support Testing the system in three water utilities in six use cases 10
SAFEWATER at a glance EU funded FP7 – security project, 10/ 2013 – 12/ 2016 Nine partners from five countries with expertise in the area of: 11
SAFEWATER at a glance EU funded FP7 – security project, 10/ 2013 – 12/ 2016 Nine partners from five countries with expertise in the area of: Sensor development (CEA, ACREO, Biomonitech) 12
SAFEWATER at a glance EU funded FP7 – security project, 10/ 2013 – 12/ 2016 Nine partners from five countries with expertise in the area of: Sensor development (CEA, ACREO, Biomonitech) Event management system (Fraunhofer IOSB) 13
SAFEWATER at a glance EU funded FP7 – security project, 10/ 2013 – 12/ 2016 Nine partners from five countries with expertise in the area of: Sensor development (CEA, ACREO, Biomonitech) Event management system (Fraunhofer IOSB) Event detection system (Decision M akers) 14
SAFEWATER at a glance EU funded FP7 – security project, 10/ 2013 – 12/ 2016 Nine partners from five countries with expertise in the area of: Sensor development (CEA, ACREO, Biomonitech) Event management system (Fraunhofer IOSB) Event detection systems (Decision M akers) Simulators (3S Consult, Fraunhofer IOSB) 15
SAFEWATER at a glance EU funded FP7 – security project, 10/ 2013 – 12/ 2016 Nine partners from five countries with expertise in the area of: Sensor development (CEA, ACREO, Biomonitech) Event management system (Fraunhofer IOSB) Event detection systems (Decision M akers) Simulators (3S Consult, Fraunhofer IOSB) M anagement of Drinking Water Networks (Hagihon, AdA, Zürich) 16
SAFEWATER at a glance EU funded FP7 – security project, 10/ 2013 – 12/ 2016 Nine partners from five countries: Sensor development (CEA, ACREO, biomonitech) Event management system (Fraunhofer IOSB) Event detection systems (Decision M akers) Simulators (3S Consult, Fraunhofer IOSB) M anagement of Drinking Water Networks (Hagihon, AdA, Zürich) coordinator scientific coordinator 17
Solution Concept Operator, M anager Sim Simulators 19
New CBRN Sensors 1) Rapid detection of E. coli (ACREO, Sweden) 2) Chemical toxicity detection (biomonitech, Israel) 3) Radioactivity detection ( α , β ) (CEA, France) 20
New CBRN Sensors 1) Rapid detection of E. coli (ACREO, Sweden) 2) Chemical toxicity detection (biomonitech, Israel) 3) Radioactivity detection ( α , β ) (CEA, France) Rapid detection of Chemical toxicity detection Radioactivity E.coli (ACREO) (biomonitech) sensor (CEA) 21
Rapid detection of E. coli E. coli bacteria are labeled with fluorescent nanoparticles via specific antibodies Clusters of E coli bacteria in sewage water Antibody stained E coli bacteria 22
Rapid detection of E. coli T empered Water sample in Labelling agent in Incubation loop Turbulent P1 P2 mixing Flow Cytometer M easurement P3 (counts E coli/ ml) Cleaning agent in P= pump Waste out 23
Rapid detection of E. coli Sensor Prototype – tested at Hagihon (J erusalem) and WVZ (Zurich) Graphic user interface 24
Chemical Toxicity Detection Chemical toxicity detection by luminescent marine bacteria (e.g. pesticides, herbicides, heavy metals) Deionised Water + toxic substance Luminescence 300000,0 250000,0 200000,0 RLU 150000,0 100000,0 50000,0 0,0 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 1,60 1,80 Chemical concentration (mg/ L) 25
Chemical Toxicity Detection Sensor Prototype – tested at Hagihon (J erusalem) and AdA (Portugal) Spiked Toxic Chemical relative units time (min) 26
Radioactivity Detection Detection of α - and β -radiation Based on a scintillating optical fibers bundle M iniaturised Photomultiplier Tube (PM T) Scintillating optical fibers bundle Photonmultiplier First prototype 27
Radioactivity Detection Sensor Prototype – tested at Hagihon (J erusalem) 28
Radioactivity Detection Sensor Prototype – tested at Hagihon (J erusalem) Alarm 29
Sensors with wireless Data Transmission Aim: Obtain more water quality data from the network Data transmission via GSM Secure cloud based data storage HaGihon (Jerusalem) Water Utility Zürich 30
Test-sites at Water Utilities Test Site at HaGihon (J erusalem) Test of the Safewater Software modules (EDS, EM S, Simulators) Test of Safewater chemical sensor, biological sensor & commercial sensors T oxicity sensor (biomonitech) 31
Test-sites at Water Utilities Test Site at Águas do Algarve (Portugal) Test of the Safewater Software modules (EDS, EM S, Simulators) Test of Safewater chemical sensor & commercial senors Commercial sensors 32
Test site at CEA (France) Experiments: α , β radiation material mixed with water 33
Test Network in Zurich Test of all SAFEWATER software modules Test of E. coli sensor & commercial sensors T est network Zurich: 150 m pipe length, 5 m width Injection pump 34
Test Network in Zürich Outflow Contaminant Outflow Inflow Open valve Closed valve Water quality sensor Injection of contaminant 35
Test Network in Zürich Injection with fluorescenting material, salt and sewage water 11:20 11:30 11:40 11:50 12:00 12:10 12:20 36
Incomplete M ixing at Crossing Investigation of mixing at crossing under various flow conditions 37
Detection of sewage intrusion Lab tests at Wasserversorgung Zürich Dilution experiments with waste water to determine the sensitivity of different parameters for the detection of faecal contamination in tap water Biological parameters are most promising to detect faecal contaminations: Enterococcus, E. coli and Heterotrophic Plate Count (HPC) HPC: method that measures colony formation on culture media of heterotrophic bacteria in drinking water TCC WWTP Effluent Enterokokkus Prefiltered WW M ost sensitive E. Coli parameters HPC Ammonium DOC UV254 Turbidity pH conductivity 0.01 0.1 1 10 100 minimum % of waste water in tap water to show a significant alteration of the corresponding parameter in lab analysis Sewage concentration [in arbitrary units] 39
Event M anagement System (EM S) Operator, M anager Simulators Sim 40
Event M anagement System Web based, user friendly platform that handles events reported by the Event Detection System 41
Event M anagement System Interface to offline & online simulators with GIS-based visualisation of results 42
GIS-based Display of Simulation Results Display options for water quality simulation results: (1) concentrations or (2) time for spread of contamination Injection point Spread during hour 1 Spread during hour 2 Spread during hour 3 43
GIS-based Display of Simulation Results Display of flow and flow directions Flow direction Link to alarm info Legend 44
Event Detection System Operator, M anager Simulators Sim 45
Event Detection System Combines information from multiple sensors in order to detect anomalies in water quality measurements 46
Event Detection System The Event Detection System uses several techniques: User defined: Violation of single-variable limits (user-defined and those learned by the EDS) Violation of known rates of change for measurements / detection of elevated noise levels Violation of rules (defined by the User) Appearance of known bad past situation(s) Learned by the Event Detection System: Appearance of rare combination(s) Abnormal patterns in multivariable combinations Detection of unexplained variance between different monitoring stations 47
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