Transcript The Potential For Automated Monitoring And Network Models

Modeling Water Quality In Drinking
Water Distribution Systems: Its
Potential for Enhancing Water
Security
Will Discuss
 Concern over the vulnerability of water
distribution systems to security threats
 Basics of water quality modeling and its
application in drinking water networks
 Two examples of water quality modeling
for water security
 The future use of water quality modeling
as part of a decision support framework
9/11 Raised Concerns About Critical
Infrastructure in the US
 Water supply was identified as critical
infrastructure
 It is now general consensus that the
vulnerability of drinking water networks
systems to security threats is a major
concern
 Utility industry has also recognized the
importance of environmental monitoring
in maintaining water security
Drinking Water Systems In The U.S.
 There are 54,000 community water
systems in US serving 264 million people
 79% of the population receives drinking
water from large utilities (serving 10,000 or
more), representing 14% of the systems
 21% of the population receives water from
small utilities (serving less than 10,000
people) representing 86% of the systems
U. S. Water Supplies Have Common
Characteristics
Water source
 A lake, reservoir, river or ground water from an aquifer

Surface supplies generally have conventional
treatment facilities and disinfection
Ground water systems
 May have full range of treatment technology but some
practice chlorination only or do not disinfect at all
Transmission systems
 Tunnels; reservoirs and/or pumping facilities; and
storage facilities
Distribution system
 Carrying finished water pipes to consumer
Distribution System is Most
Vulnerable Part of Water System
 Community water supplies designed to deliver
water under pressure and most of the system
capacity is reserved for fire fighting purposes
 Could damage or destroy a tank or reservoir
 Potential for the deliberate introduction of
contaminants into a distribution system (back flow,
cross connections)
 Need to be able to predict contaminant transport
pathways and to measure concentration of
contaminants in networks
 Cyber attack could also provide a serious threat to
an utilities operations. However many SCADA
systems are not connected to the Internet
Predicting Contaminant Movement In
Drinking Water Distribution Systems
 Movement of water in distribution systems
is complex
 The ability to predict movement is still
relatively crude
 Also need to be able to predict changes in
concentration of contaminants
 Few attempts to integrate monitoring and
modeling
Contaminants May Be Conservative, or
May Experience Decay or Growth
 Changes may take place in the bulk phase
or at the pipe wall
 Quality may be influenced by:
 Cross Connections
 Failures at the Treatment Barrier
 Transformations in the bulk phase
Schematic of Chemical and Microbiological Transformations at the Pipe Wall
Schematic of Chemical and Microbiological Transformations in Drinking Water
Water Quality Modeling Principles
 Conservation of mass within differential lengths of pipe
 Complete and instantaneous mixing of the water entering
pipe junctions
 Appropriate kinetic expressions for the growth or decay of
the
substance as it flows through pipes and storage facilities
This change in concentration can be expressed by a
differential
equation of the form:
dCij
dt
 vij
dCij
dx
 kijCij
Where:
– Cji is the substance concentration mass/ft3)
at position x and time t in the link between
nodes i and j
– vij is the flow velocity in the link (equal to
the link’s flow divided by its cross-sectional area
in ft/sec
– kij is the rate at which the substance reacts
within the link (mass/ft3/sec)
Storage tanks can be modeled as completely mixed,
variable volume reactors where the change in volume
and concentration over time are:
dVs
  Qks   Qkj
dt
k
j
d (Vs Cs )
  QksCks
  j Qsj Cs  kil (Cs )
k

L
dt
Where:
- Vs is the volume (ft3) of the tank
- Cs is the concentration in tank s
The following equation represents the concentratio
leaving the junction and entering a pipe:
Cij
Q C


Q
kj
x 0
Cij
x 0
kj x  L
kj
Where:
Cij
x 0
is theconcentration atthebeginningof thelink
and
Ckj
xL
is theconcentration attheend of thelink
Model Interaction
Water quality models are generally piggy
backed on hydraulic models.
Hydraulic Model
Flows and velocities
Water Quality Model
Water quality results
Will Use EPANET To Illustrate the Need
For Integrating Modeling and
Monitoring
 First example will be the
application of EPANET to North
Marin Water District in California
• Illustrates the linkage between
monitoring and modeling
 Second example is the waterborne
outbreak in Cabool Missouri in
1990
• Forensic application of modeling
Modeling of Contaminants
 First field study using EPANET in
North Marin California
 Modeled chlorine residual
propagation and THM formation
 Applied to two source system
North Marin Water System
 Located near Novato , California
 Serves over 50,000 people
 Virtually no rainfall during warm summer
months
 Uses two sources of dramatically different
quality
Day 1, 5:00 AM
RIVER
LAKE
Trace Lake
20.00
Node 120
40.00
60.00
Node 105
80.00
percent
NORTH MARIN NETWORK
ug/Liter
TTHM Formation for Stafford Lake and North Marin
Aquaduct
180
160
140
120
100
80
60
40
20
0
Stafford Lake
North Marin
Predicted Stafford Lake
Predicted Aquaduct
0
3
6
9
12 15 18 21 24 27 30 33 36 39 42 45
Time in Hours
TTHMs in ug/L
180
160
140
120
100
TTHMs at node 120
80
60
40
20
0
0
10
20
30
Time in Hours
40
50
Actual vesus predicited TTHMs at node 120
0
0
0
0
Actual TTHMs
Predicted TTHMs
0
0
0
0
0
0
1
3
5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
Time in hours
Actual TTHMS vesus time at node 105
Actual TTHMS
180
160
140
120
100
80
60
40
20
0
0
10
20
30
Time in hours
40
50
60
TTHMs versus time in hrs for Node 105
0
0
0
0
0
0
0
0
TTHMs
Predicted TTHMs
0
0
0
2
4
6
8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48
Time in hrs
EPANET Applied to Waterborne
Outbreak in Gideon Missouri in 1993
 Salmonella contamination occurred in
municipal tank due to failure of hatches
and vents
 Taste and odor complaints caused water
officials to start flushing program
 Out of population of approximately 1000
people, 440 became ill and 7 people died
 Used model to track outbreak and identify
source
Municipal Water System in Gideon Was
Old and in Disrepair
 Tuburculation and corrosion in the
distribution pipes was a major problem
 Two municipal tanks
 Another tank was located on the
property of the Cotton Compress
which was the major employer in the
area
Waterborne Outbreak
 On November 29, 1993 Communicable
Disease Coordinator for the Missouri DOH
became aware of two high school students
with culture confirmed Salmonellosis
 Within two days five additional patients were
hospitalized with confirmed salmonellosis
 Missouri Department of National Resources
was informed that DOH suspected a water
supply link to outbreak
 DNR samples were positive for fecal coliform
 City of Gideon was required to issue a boil
water order
Number of Absentees in Gideon Schools
Homes with Cases Between 11/23 – 11/28 and 11/29 – 12/10 1994
in Gideon, Missouri
Comparison of Early Confirmed
Cases and Salmonella Positive
Sample Versus Penetration of
Tank Water During First Six
Hours of Flushing Program
Current Status of Water Quality/Hydraulic
Models
 Increasingly sophisticated
 Applied to exposure studies
• ATSDR study on contaminated
ground water
 Much research into modeling changes
in water quality
• Formation of DBPs and Chlorine
Residuals
 Tank Mixing Models
EPA Research in Real Time Monitoring
Systems
 First EPA effort was development of sensors for
temperature, chlorine residual, fluoride and
nitrate data with Battelle
 Asked to assist during MCL violation in
Washington DC
 Initiated research on development of sensors and
probes for chlorine residual, pH and temperature
using pipe loops
 Applied to DC water system
 Future efforts should focus on integrating
modeling and monitoring
Summary and Conclusions
 Water systems have been classified
as critical infrastructure
 Identified as potentially vulnerable
 Contaminant Propagation Can be
Modeled and there are various
models available
 EPANET is a public sector model
that has become widely used
Summary and Conclusions
 EPA has been conducting research into
sensor development
 Applied to operation of small package
plants
 Extended to chlorine residual monitoring
in Washington DC system
 Future research will focus on integrating
remote sensing and water quality
modeling