United States
                   Environmental Protection
                   Agency
Risk Reduction Engineering
Laboratory
Cincinnati OH 45268
                   Research and Development
EPA/600/S2-90/030 Aug. 1990
&EPA          Project  Summary
                    Operation  of Water  Distribution
                    Systems  to  Improve  Water
                    Quality

                    Robert M. Clark, Judith A. Coyle, Richard M. Males, and Walter M. Grayman
                    The  quality of drinking  water can
                   change dramatically  between the
                   point of discharge from the treatment
                   plant and the point of consumption.
                   To  study these changes  in  a
                   systematic manner,  EPA's Drinking
                   Water Research  Division  in
                   conjunction with the  North  Penn
                   Water  Authority (NPWA) in Lansdale,
                   PA, developed and  field tested
                   contaminate propagation models
                   over a 3-1/2 yr period.
                    Temporal and spatial variations in
                   water quality were found to be much
                   greater than expected.  Steady-state
                   predictive modeling of water  quality
                   provided insight into  overall  water
                   quality variations and patterns within
                   the distribution system,  which
                   received water from multiple sources.
                   Point prediction of water quality was,
                   however, limited. Dynamic modeling,
                   though more difficult, provided better
                   insights into system behavior and
                   more accurately reflected changes in
                   water quality.
                    This Project Summary was
                   developed by EPA's Risk  Reduction
                   Engineering Laboratory,  Cincinnati*
                   OH, to announce key findings of the
                   research  project  that  is  fully
                   documented in  a separate report of
                   the  same title  (see Project Report
                   ordering information at back).

                   Introduction
                    The  research  summarized here
                   explored  the possibility  of  using
                   operational modifications  of the water
distribution system (i.e., changes  in
pumping, .valving,  etc.), rather than
additional treatment, to  improve
delivered, quality water to the consumer.
The NPWA uses multiple water sources,
including a purchased,  treated, surface
water source and numerous  wells. The
purchased  water has  a significantly
different  chemical profile than does  the
well water, and water quality  also  varies
among the wells. Water from  different
sources  mixes and blends  within  the
distribution system, changing with time.
All of this results in variations in  the
quality   of water delivered  to the
consumer.
  This U.S.  Environmental  Protection
Agency's Drinking Water  Research
Division  has developed the Water Supply
Simulation  Model  (WSSM),  computer
programs designed  to create  and
maintain a data  base  of information
concerning a water distribution system,
so that  a variety of models could  be
applied to that data base. The component
of the WSSM  known as the "Solver"
algorithm was developed to predict
mixing of water in a distribution system,
based on steady-state assumptions and
known  hydraulic flow patterns.  The
WSSM had been applied on a theoretical
basis", but had  not been tested against
field data. This study was designed  to
apply the predictive model of mixing to
the NPWA  system,  field test the
predictions, propose operational  changes
to modify the flow and  mixing patterns,
and test  these modifications.  During  the
study, after a  short-term  sampling

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program indicated the dynamic nature of
quality variations, a dynamic water quality
model (DWQM)  was developed. Some
operational  modifications  have  been
made in the NPWA system based on the
data developed in this project.
  The report details the significant use of
computer modeling and the development'
of a  number  of generally  applicable
models.  It also details the testing of two
automated samplers that are capable of
preserving volatile organics.   -

The North Perm Water Authority
  The NPWA serves 14,500 customers in
19 municipalities in Montgomery County,
north  of Philadelphia. Water  sources for
the 5-mgd system include  1  mgd  of
treated surface water purchased from the
Keystone  Water Company and 4  mgd
from 40 wells  operated by NPWA. The
system  is largely  contiguous;  a few
unconnected satellite systems  were not
modeled  in this  study. The system has
five  storage tanks  and  two pumping
stations.  Figure  1,   a schematic
representation of the 225 miles of pipe in
the  contiguous NPWA  distribution
system,  shows the location of wells, the
Keystone "tie-in," and the three pressure
zones (Souderton Zone, Lansdale  Low
Zone, and Hillcrest Zone):  Pipe sizes
range from  3  to 24 inches but  the
majority  of  the pipe is cement-lined
ductile iron or unlined cast iron.
  Surface  water  enters the  NPWA
system at the Keystone tie-in. The rate of
flow into the system is determined by the
elevation  of  the tank  in  the Keystone
system and  by a throttling valve at the
tie-in.  Flow,  which   is  monitored
continuously, is  relatively  constant; the
throttling  valve  is adjusted  seasonally.
Water flows  into  the  Lansdale low
pressure zone and from there enters the
Lawn~"Avenue  tank, from which  it  is
pumped   into   the  Souderton  Zone.
Additional water, solely derived  from
wells in  the Hillcrest  Pressure  Zone,
enters the Lansdale system at the Office
Hillcrest transfer point. Except for unusual
and extreme circumstances, such as fire
or main breaks, water does not flow from
the Souderton Zone in the Lansdale Low
Zone, nor from the Lansdale Low  Zone
into the  Hillcrest  Zone.  This  study
emphasized  modeling  water  flow in the
Lansdale Low Zone, which is the largest
portion of the distribution  system.
  The well  pumps operate  on  time
clocks.  Although  some  wells pump
continuously,  some are  not pumped
during the late evening and early morning
hours. At these times,  Keystone water
 moves  further into the  distribution
 system.
   The  chemical  characteristics of
 Keystone water differ from those of the
 well waters. Keystone water contains total
 trihalomethanes (TTHM)  at  significantly
 higher levels than  does  the  well water.
 Certain wells  show the  occurrence of
 trichloroethylene  (TCE),  or  cis-1,2-
 dichloroethylene (cis-1-2-DCE), or both.
 Inorganic chemicals also vary from well
 to well  and  between  the  wells  and
 Keystone water.
   The NPWA distribution system is well
 instrumented,  with  detailed  continuous
 records available on well pumpage, tank
 heights, and flow at various  locations with
 the system. In  addition, NPWA maintains
 a detailed water quality  sampling
 program, both  at  sources and  in  the
 distribution system; thus, there  is
 significant historical data  available on
 system water quality.


 Description  of the Study

 Steady-State Prediction of
 Mixing
   With the use of WSSM, a steady-state
 prediction of mixing within the distribution
 system was obtained. This effort  required
 the  following steps: after establishing a
 WSSM  data  base for  the  NPWA
 distribution  system and  developing and
 parameterizing a hydraulic  model of the
 NPWA distribution system as  well as
 hydraulic  "scenarios,"   hydraulic
 information obtained  from selected
 scenarios was  stored within  the  WSSM
 data base. Running the Solver module of
 the WSSM  provided  steady-state
 prediction of water mixing.
   The steady-state algorithm as applied
 in this case did not take into account the
 concentration of Keystone water resident
 in the tanks. According to  the late  night
 scenario, at least some  Keystone water
 will enter the tanks  and then be
 discharged  into the distribution system as
 another source of Keystone water. This is
 a more complex  analysis, but, in the
 majority of the  hydraulic  scenarios
 selected in  this study, tanks  were filling,
 rather than discharging to the system.
   The  steady-state analysis  inherently
 cannot take into account water already in
 the  pipes of the distribution system. For
 example, the  late  night  scenarios, in
- which wells are off, results in  Keystone
 water  flowing throughout the system. In
 fact, much  of  the water in the  pipes at
 that time is well water; the pipes act as a
 reservoir for this water, even though the
 wells  are off.  Obviously, blending takes
place, rather than water being either
well water or fully  Keystone water.
volume  of  water in the pipes  of  iw
distribution system (as represented in the
WSSM,  i.e., neglecting smaller pipes)
was calculated to be 2.8 million gallons,
or over half of the average daily supply of
5  million   gallons  for  the  NPWA
distribution system.  The reservoir effect,
which results when the  wells are shut off
at night and  Keystone water enters pipes
partially filled with well  water, is likely to
be significant and cannot be  taken  into
account in  traditional  steady-state
modeling. This further  confirms  the
desirability of dynamic quality modeling.

Comparison  of Steady-State
Prediction with Historical Data
  Solver  module  predictions  were
compared with  water quality  sampling
data to  assess  the spatial  variations in
quality. Average values for specific water
quality variables,  at defined  locations,
were  calculated.  Because the  TTHM
formation of  the purchased surface water
was assumed to have achieved a steady-
state  relationship and  the  TTHM
production for the  other sources in the
system was  assumed to be zero, TTHMs
were used as a predictor of mixing  and
blending of  surface  with well water,
Solver predictions in general agree
the analyzed  historical  data. "Point-
prediction"  capacity (the ability of the
model to accurately estimate the level of
a pollutant  at  a  specific point in  the
distribution  system)  was,  however,
limited.

Short- Term Sampling Program
  A short-term, pilot sampling  program
was  undertaken to characterize more
accurately  the spatial (space)  and
temporal (time)  variations of quality in the
water distribution   systems.  Six sites
throughout the  distribution system  were
sampled at  4-hr  intervals  for  a 36-hr
period.  This sampling  program showed
significant variations in TTHMs at the
Keystone surface  water  source  and
obvious blending and mixing of the  well
and  surface water  in the distribution
system  and, as predicted by the steady-
state  modeling analysis, one  location
alternately  fed  by  well  water and  by
surface water.
  The  pilot sampling  program  clearly
showed the dynamic nature  of quality
variation within the system and led to
methods to  predict dynamic  quality. In
addition, the difficulty in obtaining manual
samples led to  development  of
automated samplers.
                                                          2

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Dynamic Quality Modeling
•forts

Sequential Steady-State
Modeling
  The  initial  attempt to  model the
dynamic  quality variations found  in the
sampling  study  was   based  on
successively applying  the steady-state
Solver  technique  to different demand
patterns and  boundary conditions.  This
technique was  implemented   in
developing the  quality  model  by
designing different steady-state hydraulic
scenarios, which were representative, of
different time periods within the sampling
study,  and successively predicting
hydraulics  and the associated  water
quality  for  each period. This  technique,
called sequential steady-state  modeling,
again yielded reasonable results in terms
of overall  patterns.  Theoretically,
however, this approach fails to take into
account the water that is  resident in the
pipes of the distribution system which is
obviously in a blended/mixed  state, and
is extremely  cumbersome to  handle
logistically.
Development of Dynamic Water
Quality Model/Flow Tracing
Algorithm
  A  dynamic  water  quality  model
(DWQM) was developed that would better
represent the variations observed in the
water quality.  DWQM is based  on a
routing  technique  that continually
accounts for the quality of water in  pipes
within the distribution system. As with the
steady-state model, the DWQM relies on
externally available, detailed hydraulic
information relating to flows in pipes and
at nodes available for  each time step in
the quality  simulation. The  DWQM is
based on flow tracing  rather than on the
simultaneous equation  solution used in
the Solver  algorithm. When the DWQM
was tested against the conditions of the
sampling study, an  extended-period
hydraulic simulation was  developed that
showed good agreement  between
predicted and  actual tank levels and the
pressures in the system.  This  was then
used to model the quality  variations
which again showed  good  agreement
over the 36-hr sampling run.
Conclusions and
Recommendations

Steady-State Predictive
Modeling
  Based on the results to date, steady-
state predictive modeling appears to be a
reasonable first step to characterize the
distribution of water quality in multisource
systems.  Although  point  prediction
capability is probably not accurate, and
probably  highly sensitive  to  hydraulic
assumptions,  general  trends can  be
established, and then  verified, through
field sampling.
  Steady-state  prediction might be better
suited to  systems  that are less dynamic
in terms of operation, with fewer sources,
than is the NPWA  distribution system.
Steady-state assumptions might be more
consistent with  a  less dynamic system.
Because  of the manner  in  which the
NPWA system was   developed,  by
connecting of  a  number  of  separate
systems,  it is  somewhat disjointed, a
number of  portions of  the  system are
connected by  a single pipe.  Once the
hydraulic solution establishes  flow
direction, the  system is  effectively
partitioned, at least in certain areas, into
zones of uniform concentration.
Dynamic Water Quality Model
  Results of  the  pilot  sampling study
clearly showed the spatial  and temporal
variability of water quality  in the  NPWA
distribution  system.  Because  little
information  was available on  short-term
spatial/temporal variability for distribution
systems  (most monitoring  strategies
involve taking samples at daily, monthly,
or quarterly  intervals,  at a  selected
number  of  sites)  within-day quality
variations were seldom noted.
  Although  steady-state prediction  of
quality does provide certain insights into
system behavior,  dynamic  water  quality
models, though more demanding of input
data  describing   the  system,  more
accurately  reflect  changes  in  water
quality.
  The DWQM developed in this study is
relatively  simple to use and can easily be
extended to more complex situations  of
nonconservative constituents. As applied
to the NPWA pilot run,  good agreement
between  predicted and actual results
were found.
  When  using  DWQMs,  two  major
problems arise: the need to manage the
large quantity of data that the models use
and the need  for improved methods  of
data analysis and display.
  Dynamic  water quality  models  both
generate and use large quantities of data.
Detailed  descriptions  of  dynamic
hydraulic behavior are required,  and the
models  can generate  extensive
information  on within-pipe and nodal
water quality over time. Improvements in
data  handling,  and  development  of
techniques  for analyzing and  displaying
data reflecting  spatially varying, dynamic
water quality, would contribute greatly to
ease  of  using dynamic water quality
models.

Monitoring Needs
  A clear result from  this research is the
need  to obtain  more  representative
monitoring  results than  are  normally
acquired   from   distribution  system
sampling.  Contaminant  values can  vary
greatly over a relatively  short  time  at a
given point. There are  also  no doubt
weekly  and yearly  cycles that, when
combined  with  hydraulic  and  mixing
variations, will have great  effect on the
contaminant levels at a given  point in a
distribution  system. Existing compliance
monitoring strategies probably fail to truly
reflect population exposure because they
are based on  assumptions about quality
behavior in distribution  systems  that
imply  essentially   steady-state
characteristics.
  The use  of  automatic samplers,  such
as  those developed  and tested in this
study, should  prove  to be  of  significant
value in rapidly characterizing the degree
of temporal/spatial variability of water
quality  in  distribution  systems.  It  is
expected these samplers can,  with minor
modifications, be useful field instruments.
Extension  of the approach to bacterio-
logical sampling  would also prove  very
useful.
  Having the  tools  to  predict  time-of-
travel between points in a system and to
estimate the quality of water provided to
any point from any source would  allow for
realistic  water  quality  monitoring
strategies.
  The full  report  was  submitted  in
fulfillment of Cooperative Agreement No.
811011  by North Penn  Water Authority
under the  sponsorship  of  the  U.S.
Environmental  Protection Agency.

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The EPA author, Robert M. Clark (also the Project Officer, see below), is with Risk
   Reduction Engineering Laboratory, Cincinnati,  OH 45268;  Judith A. Coyle is
   with North Penn Water Authority, Lansdale, PA 19446; Richard M. Males is with
   RMM Technical Services, Inc., Cincinnati, OH 45208; and Walter M. Grayman is
   with Walter M. Grayman Consulting Engineers, Cincinnati, OH 45229.
The complete report, entitled "Operation of Water Distribution Systems to Improve
   Water Quality," (Order No. PB90-246 539/AS; Cost: $17.00, subject to change)
   will be available only from:
       National Technical Information Service
       5285 Port Royal Road
       Springfield, VA 22161
       Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
       Risk Reduction Engineering Laboratory
       U.S. Environmental Protection Agency
       Cincinnati, OH 45268
 United States
 Environmental Protection
 Agency
Center for Environmental Research
Information
Cincinnati OH 45268
      BULK RATE
POSTAGE & FEES PAID
         EPA
   PERMIT No. G-35
 Official Business
 Penalty for Private Use $300
 EPA/600/S2-90/030

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