DESIGN SCHEME FOR WATER QUALITY MONITORING …serialsjournals.com/serialjournalmanager/pdf/1345287637.pdf · Design Scheme for Water Quality Monitoring in a Distribution Network•

IJED: Vol. 9, No. 1, (January-June 2012): 69-81

1 Professor, Department of Water Resource Development & Management, Indian Institute of Technology,Roorkee, India.

2 M.Tech Student, Department of Water Resource Development & Management, Institute of Technology Roorkee,India.

3 Research Scholar, Department of Water Resource Development & Management, Institute of Technology, Roorkee,India

([email protected], [email protected], and [email protected])

DESIGN SCHEME FOR WATER QUALITYMONITORING IN A DISTRIBUTION NETWORK

M. L. Kansal1, Tandin Dorji2 & Surendra Kumar Chandniha3

Abstract: The ultimate goal of any water supply scheme is to provide safe and adequate drinkingwater to consumers. The water quality may get deteriorated in the distribution network andhence need monitoring. However, it is neither feasible nor economical to monitor each andevery node in distribution network. In this paper, a methodology based on the hydraulics of flowand the concept that if the water quality at a particular demand node is good, then the waterquality at its upstream nodes is also good, is used to design a monitoring scheme for a waterdistribution network. The methodology has been illustrated using a real water distribution networkfor a part of the Manendragarh city water distribution network located in Koriya district ofChhattisgarh state in India. Depending on the resources available, one can decide the locationof monitoring station in the distribution network.

Keywords: Water Distribution Network, Quality Monitoring, Water Fraction Matrix and DemandCoverage.

INTRODUCTION

Generally water quality is ensured when it leaves the water treatment plant. However, waterquality deteriorates as it moves in the distribution network due to bulk decay or due to wallreaction in the pipelines. Therefore, in order to supply water of good quality at consumer’s tap,it is important to monitor water quality at various locations in the distribution network, OstfeldA., Kessler A., and Goldberg I. [6, 7]. A major managerial and technical barrier exists becauseof improper guidelines for identifying suitable monitoring locations in a Water DistributionSystem (WDS). It has been observed that the selection of monitoring locations is based more oneasy accessibility rather than on any scientific reasoning. The preferred locations are those thatcan provide 24 hours access, Ostfeld A., and Salomons E [8, 9]. Ideally speaking, in order tohave 100 % reliable water supply, one should monitor each and every node in the distribution

70 • M. L. Kansal, Tandin Dorji and Surendra Kumar Chandniha

network. However, it is neither feasible nor economical to do this. One can use the logic that ifthe water quality at a particular node is good, then the water quality at its upstream nodes willalso be good as the water has reached that particular node via upstream nodes. Keeping thislogic in mind, one can develop a monitoring scheme which is representative of the whole network.

Recently, the procedure of selecting the monitoring stations has drawn wide attention.The most frequently chosen stations are those locations that are in low-demand zones and whichare more vulnerable from a water-quality point of view. However, in most of the situations, it isnot possible to rank the vulnerability of various locations. Thus, in a water supply network,where the demand locations are of unknown vulnerability, some scientific approach is neededto identify the number and location of monitoring stations. One such scientific approach suggestedby Lee and Deininger [4, 5] is based on the concept of demand coverage (DC). The term DC isused to represent the percentage of network demand monitored by a particular monitoring station.

Lee and Deininger [4] have used the information obtained from hydraulic analysis of thenetwork to identify the pathways such that the water quality of a large portion of the network isassessed by installing a few sampling stations. The information obtained from the pathways(in terms of a water fraction matrix) is then converted into an integer programming problemunder a chosen coverage criteria. By this method, the lowest level of knowledge occurs whenonly a very small fraction of the water passes through the node that was called’ ‘any fraction.’However, for a large network this method becomes highly cumbersome and difficult to handlebecause of the large dimensionality of the problem. The present study as suggested by KumarA., Kansal M. L. and Arora G. [2] shows that instead of integer programming formulation a farmore simple procedure can be used for identifying the locations of monitoring stations. Theprocedure is based on the hydraulics of flow and is illustrated using a real water distributionnetwork.

CONCEPT USED

Strictly speaking, monitoring location at a node covers water quality of that node only. If Di is

the demand at monitoring location and D is the total demand of the system, then, by monitoringat the ith node, the water quality of the fraction D

i/D of the network demand is known. Thus, to

cover the entire demand of the network, practically every demand node is to be sampled. However,the number of sampling locations for detecting internal decay of water quality can be significantlyreduced if the following reasonable assumption is made:

In the absence of any quality boosting measures (such as BC stations), the quality of waterdeteriorates with the passage of time as it flows in the network from the source to the variousdemand nodes. In other words, nodes that are closer to the source (in terms of travel time)receive better quality water than the nodes away from the source. Under this assumption, it canbe stated that if water quality at a given node is good, then it must be good at all those upstreamnodes that supply a ‘significant’ portion of water at the sampled node. Hence, a monitoringlocation is capable of providing inference of water quality of certain upstream nodes also.

If significant portion of the water received at a monitoring location has passed through aparticular node, it is reasonable to assume that the water quality at this upstream node can beinferred from the quality of the monitored node. The logic is based on the fact that the water

Design Scheme for Water Quality Monitoring in a Distribution Network• 71

collected at the jth station has partly or wholly passed from some of the upstream demand nodesand if the quality of the downstream monitored node j is good, there is every likelihood that thewater quality of the upstream set of demand nodes also must be good. However, a certainminimum water fraction must be contributed by an upstream node to be covered by the monitoringlocation. The minimum water fraction is termed here as representative coverage. For example,a 70% representative coverage criterion indicates that an upstream node is assumed to be coveredby the monitoring location, if 70% or more of the inflow to the monitored node has passedthrough this particular upstream node. A suitable representative coverage should be judiciouslyselected by the decision makers to represent adequate coverage of an upstream node by themonitoring location.

METHODOLOGY

The method is based on identifying monitoring locations that would yield maximumdemand coverage as suggested by Kumar A., Kansal M. L., and Arora G. [1, 2]. The monitoringlocations are chosen in the descending order of their demand coverage i.e. the node whichhas higher demand coverage is preferred as monitoring location over the node which haslesser demand coverage. The steps to be followed for identifying the monitoring locations are asfollows:

1. For a given loading pattern, analyze the WDS hydraulically and determine the flow anddirection of flow in individual pipelines.

2. Based on the flow directions obtained, identify the upstream and downstream nodes ofthe WDS.

3. Compute the elements of water fraction matrix.

4. Choose an appropriate representative coverage criterion and deduce the coverage matrixfrom the water fraction matrix.

5. Compute the demand coverage for all the nodes.

6. Choose that node as the first monitoring location which has the maximum demandcoverage. If two or more nodes have the same demand coverage, selection of one shouldbe based on the vulnerability assessment of the nodes of the WDS.

7. For locating the subsequent monitoring location, eliminate the row corresponding tothe already chosen monitoring location from the coverage matrix. In the columnscorresponding to those upstream nodes that have been covered by the chosen monitoringstation, the value of the element should be converted to 0 (wherever the value is 1, thevalue of 1 should be put in parentheses and 0 should be written outside parentheses).

8. Again compute the demand coverage of the remaining nodes from the coverage matrix,modified as per step 7. Choose that node as the next monitoring location that now hasmaximum demand coverage. If two or more nodes have the same demand coverage,the ones with the greater degree of overlap should be selected as the monitoring location.Overlapping means that a particular node is covered by more than one monitoringlocation at a given time. The overlap of a node can be estimated from the sum of


parentheses values of 1 (as obtained after step 7) in the row corresponding to thisparticular node in the modified coverage matrix.

9. Repeat steps 7 and 8 for the further selection on monitoring locations.

Computation of Water Fraction Matrix

The demand nodes are renumbered such that water flows from a lower to a highernumbered node. This renumbering facilitates the computations and also makes the [W]matrix a lower triangular matrix that represents the water fraction of the upstreamnodes connecting the source to sampling node. The water fraction matrix [W] can then berepresented as

1 11 21 31 1

2 12 22 32 2

3 13 23 33 3

1 2 3

... ...

... ...

... ...

... ... ... ... ... ... ...

... ... ... ... ... ... ...

... ...

�

� � � ��

� ��

� � � ��

s

s

s

s s s s ss

W

W

WW

W

(1)

Where, wij = fraction of demand d

j at the jth monitored node that has passed through upstream ith

node; Wj = row vector of elements w

ij (1 � i � j) that represents the fraction of demand D

j at the

jth monitoring node that has passed through some or all of i upstream nodes. The non-zero valueof w

ij in W

j vector represents those i nodes that are in the path of water reaching the jth node;

wij = 0 means that no water from node i is reaching node j; and w

ij= 1 means that whole of

demand Dj has passed through jth node.

Computation of Demand Coverage

A percentage proportion contribution should be fixed as governing criteria for coverage of anupstream node by monitoring water quality at a downstream node. This chosen percentageproportion should represent the adequate criteria for a particular node being covered by monitoringwater quality of other node. One of these criteria may be that the node is assumed to be coveredif majority of the sampled water at chosen node has come from that particular upstream node.The logic is based on the fact that the water collected at the jth station has partly or wholly passedfrom some of the upstream demand nodes and if the quality at the downstream monitored nodej is good, there is every likelihood that the water quality of the upstream set of i demand nodesalso must be good. This is particularly valid as the downstream monitored node receives delayedwater and water quality deteriorates (TTHMs, bacteria and chlorine residuals, etc.) with timeand distance from the source. In the present study, assume that if 60% or more of the inflow toa particular monitoring station has passed through an upstream node, then it is reasonable to saythat the water quality at this upstream node can be inferred from the quality of the monitorednode. Therefore, the water fraction matrix prepared earlier is modified to a 60% coverage areamatrix in which all values of w

i,j that are � 0.6 are changed to one and all the remaining values of


wi,j to 0. Once a suitable percentage criteria is chosen, the demand coverage of any node can

then be calculated as

DCj = (r

1j r

2j r

3j … r

jj …r

sj)

1

2

j

s

D

D

D

D

� ��

(2)

where Dj represents the demand drawn at the jth node and r

ij are the elements of the row vector

corresponding to node j in the R% matrix.

The node that gives maximum value of demand coverage will be the first monitoring station.

Subsequent monitoring stations can be located by computing the DC of all the remainingnodes in the coverage matrix after eliminating the row of the chosen monitoring station and thecolumns corresponding to those upstream nodes that have been covered indirectly by the chosenmonitoring station.

CASE STUDY

A part of the water distribution network (Zone II) of Manendragarh town in Koriya district ofChhattisgarh state in India is considered. The town is situated on Katani-Gumala NationalHighway No. 78 in the North-East Part of India between 23° 02' 42" to 23° 44' 46" NorthLatitude and 81°46' 42" to 82°33' 43" East Longitude as shown in Figure 1. The network consists

Figure 1: Location of Study Area


of 23 delivery nodes and 25 pipe links with one source of supply. Piped water supply scheme inthis town was commissioned in 1956 with Hasdeo River as source flowing 2.5 Kilometer awayfrom the town. The present system is capable of supplying drinking water @42 LPCD onlywhich is quite low as compared to standard requirements. During summer (starting fromMarch-April), river gets dry and there is scarcity of raw water at the intake well. As a resultpeople of Manendragarh face shortage of water. Further, water table also goes down in summerand causing problems of inadequate drinking water in the area.

Some of the areas in towns and villages face water borne disease and the problems relatedto excess chemical components like Iron, Chloride and Fluoride, etc. which has caused problemsrelated to skin, bones, and teeth, etc. For that reason monitoring locations has to be identified inorder to monitor the water quality deterioration in the distribution network.

The layout of the network is shown in Figure 2. The network is hydraulically analyzedusing EPANET 2.0 developed by Rossman L.A. [10]. The demand at various nodes and flow inpipelines is shown in Table 1.

Table 1Demand and Flows in Distribution Network

Nodes Demand (CMD) Pipes Flow (CMD)

1 287.71 1 3205.79

2 184.90 2 110.94

3 597.89 3 2922.22

4 147.74 4 88.64

5 184.90 5 110.94

6 583.20 6 2363.907 88.99 7 53.39

8 88.13 8 52.88

9 243.65 9 208.40

10 103.68 10 62.21

11 287.71 11 1699.31

12 58.75 12 35.25

13 354.24 13 1491.44

14 317.09 14 409.03

15 103.68 15 62.21

16 412.13 16 869.87

17 221.18 17 156.56

18 102.82 18 194.4019 280.80 19 61.69

20 198.72 20 584.75

21 184.03 21 251.30

22 252.29 22 35.25

23 58.75 23 164.98

24 45.75

25 64.67


The flow directions are shown in Figure 2.

Figure 2: Zone II Water Distribution Network for Mahendragarh

The water fraction matrix [W] for the network is generated considering each node as thesampling node. The elements �

ij of water fraction matrix [W] calculated and are shown in

Appendix 1.

After generating the water fraction matrix, a representative coverage is to be fixed. In thiscase, R = 60% (0.60) is chosen. Based on this representative fraction, a 60% coverage matrix isderived from the water fraction matrix as shown in Appendix 2 (a). The binary elements ofcoverage matrix under 60% criteria are computed as r

ij = 1 if �

ij � 0.60 and r

ij = 0 if �

ij < 0.60.

The demand coverage (DCi) values are calculated for each of the 23 nodes using equation (2).

After computation of demand coverage, it is clear that for node 22, the demand coverage valueis the maximum (3055.97 m3/day out of 5342.71 m3/day). Thus, node 22 should be selected asthe first monitoring location By selecting node 22 as the monitoring location, nodes 1, 3, 6, 11,13, 16, 19 and 22 are covered as shown in Appendix 2 (a).

For locating the second monitoring location, following steps have to be followed:

(a) The row corresponding to the monitoring location 22 is to be removed for writing themodified coverage matrix. Thus, the modified 60% coverage matrix will now containrows corresponding to 21 nodes.

(b) By selecting node 22 as the monitoring location, nodes 1, 3, 6, 11, 13, 16, 19 and 22 arecovered. The elements in the column corresponding to these nodes are converted tozero (the initial value wherever 1, is put in parentheses).


For selection of the second monitoring location, the demand coverage values have to becomputed for all 21 nodal row vectors. Node 15 will be the next optimal monitoring location asit has the maximum value of demand coverage in the modified coverage matrix. The demandcovered by node 15 is 420.77 m3/day and the modes covered by this monitoring location are 14and 15 as shown in Appendix 2 (b). There is no same demand coverage in the row vectors.

Subsequently, the other optimal monitoring locations are being found out in the similarmanner as above and being presented in Table 2.

Table 2Monitoring Stations

Monitoring Stations Demand Covered in (cmd) Nodes Covered

22 3055.97 1,3,6,11,13,16,19,2215 420.77 14,1510 347.33 9,1018 324 17,1820 198.72 202 184.9 25 184.9 521 184.03 214 147.47 47 88.99 78 88.13 812 58.75 1223 58.75 23

If one has to choose only four monitoring locations in this WDS, the best locations wouldbe at nodes 22, 15, 10 and 18 having total demand coverage of 77.64% (3055.97 + 420.77 +347.33 + 324.00 = 4148.07 m3/day out of 5342.71 m3/day) as shown in Table 2.

Table 3Demand Covered by Various Monitoring Stations

Cumulative Demand covered (cmd)

Monitoring Demand Covered Demand PercentageStations in (cmd) covered (cmd) covered

1 3055.97 3055.97 57.202 420.77 3476.74 65.073 347.33 3824.07 71.584 324 4148.07 77.645 198.72 4346.79 81.366 184.9 4531.69 84.827 184.9 4716.59 88.288 184.03 4900.62 91.739 147.47 5048.09 94.4910 88.99 5137.08 96.1511 88.13 5225.21 97.8012 58.75 5283.96 98.9013 58.75 5342.71 100.00


Figure 3 shows the percentage of demand covered by individual monitoring locations.

Figure 3: Monitoring Station Vs Demand Covered

CONCLUSION

The objective of this paper was to suggest method for designing a scheme for monitoring stationsin a water distribution network for monitoring water quality. The scheme has been designed onthe basis of hydraulics of flow and by using the concept of demand coverage criteria. Also, thenodes which are representative of maximum demand nodes in terms of demand coverage areselected with top priority and nodes which are representative of themselves only are given lowpriority. Previously, this optimization problem has been solved using integer programming thattakes into account the various constraints for a given node pattern of the water distributionsystem. The integer programming problem accommodates (n + 1) constraints and (n*p + n)variables, where n is the number of delivery nodes in the distribution system and p is the numberof demand patterns. For a large network, the dimensions of the problem become very large.

This paper suggests a simplified procedure for identifying the optimal locations of monitoringstations in a water distribution system. A methodology proposed for identifying optimalmonitoring locations can provide water quality information for the maximum demand in thewater distribution system. In the case of large and complex networks such as municipal waterdistribution system, many interpretations cannot be drawn from random monitoring locationsand use of scientific method is more logical. Use of this method for identifying the monitoringlocations has advantage of scientific justification, over the random sampling process. The methodproposed in this paper identifies the monitoring locations that can provide useful informationabout the quality of water under internal decay, without the duplication or repetition of monitoringin the WDS. The proposed method can be used for determining suitable monitoring locationsfor existing WDS and after augmentation of WDS. The method can be used for those WDSwhere it can be safely assumed that there is no intrusion of external contaminants into WDS.The procedure is illustrated through an example demonstrating the superiority of the proposedprocedure.


References

Kumar A., Kansal M. L. and Arora G., Discussion on “Detecting Accidental Contamination in MunicipalWater Networks,” Journal of Water Resource Planning and Management, ASCE, Vol. 125, No. 5,pp. 208-310, (1999).

Kumar A., Kansal M. L. and Arora G. (1997), “Identification of Monitoring stations in Water DistributionSystem,” Journal of Environmental Engineering, ASCE, Vol. 123, No. 8, pp. 748-752.

Kumar A., Kansal M. L. and Arora G. (1999), “Tracking Accidental Contamination in Municipal WaterNetworks,” Proceedings of Civil and Environmental Engineering Conference New Frontiers &Challenges, 1-67 to 1-76, Bangkok, Thailand, 8-12 Nov..

Lee B.H, Deininger R. A. and Clark R. M. (1991), “Locating Monitoring Stations in Water DistributionSystem”. Journal AWWA, 83(7), pp. 60-66.

Lee B.H. and Deininger R. A. (1992), “Optimal Location Monitoring Stations in Water Distribution System”.Journal Environmental Engineering, ASCE, 118(1), pp. 4-16.

Ostfeld A., Kessler A. and Goldberg I. (2004), “A Contaminant Detection System for Early Warning inWater Distribution Networks”. Engineering Optimization, 36, 6, 525-538.

Ostfeld A., Kessler A., and Gideon Sinai (1998), “Detecting Accidental Contaminations in municipalwater networks”. Journal of Water Resources Planning and Management, ASCE, Vol. 124, No. 4,pp. 192-198, August.

Ostfeld A. and Salomons E. (2003), “Early Warning Monitoring Systems for Water Distribution SystemSecurity”, Proceedings of the Annual Water Resources Planning and Management, ASCE Conference,Philadelphia, Pennsylvania, USA, June.

Ostfeld A., and Salomons E. (2004), “Optimal Layout Early Warning Detection Stations for WaterDistribution Systems Security”, Journal of Water Resources Planning and Management, ASCE, Vol.130, issue 5, pp. 377-385, October.

[10] Rossman L. A. (2000), “EPANET users manual”, EPA/600/R-00/057 Risk Reduction Eng. Lab.,USEPA, Cincinnaati, Ohio.

Design Scheme for Water Quality Monitoring in a Distribution Network• 79A

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Design Scheme for Water Quality Monitoring in a Distribution Network• 81A

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0(1)

00

00

0(1)

00(

1)1

317.

09

150(

1)0

0(1)

00

0(1)

00

00

0(1)

00(

1)1

142

0.77

160(

1)0

0(1)

00

0(1)

00

00

0(1)

00(

1)0

00(

1)0.

00

170(

1)0

0(1)

00

0(1)

00

00

0(1)

00(

1)0

00(

1)1

221.

18

180(

1)0

0(1)

00

0(1)

00

00

0(1)

00(

1)0

00(

1)1

132

4.00

190(

1)0

0(1)

00

0(1)

00

00

0(1)

00(

1)0

00(

1)0

00(

1)0.

00

200(

1)0

0(1)

00

0(1)

00

00

0(1)

00(

1)0

00(

1)0

00(

1)1

198.

72

210(

1)0

0(1)

00

0(1)

00

00

0(1)

00(

1)0

00(

1)0

00(

1)0

10

184.

03

230(

1)0

0(1)

00

0(1)

00

00

0(1)

00(

1)0

00(

1)0

00(

1)0

00(

1)1

58.7

5

Documents

DESIGN SCHEME FOR WATER QUALITY MONITORING …serialsjournals.com/serialjournalmanager/pdf/1345287637.pdf · Design Scheme for Water Quality Monitoring in a Distribution Network•