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INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 2, No 3, 2012

© Copyright 2010 All rights reserved Integrated Publishing services

Research article ISSN 0976 – 4380

Submitted on December 2011 published on February 2012 868

Groundwater quality assessment with two multi-criteria decision making

methods LI Peiyue, WU Jianhua, QIAN Hui

School of Environmental Science and Engineering, Chang’an University

No. 126 Yanta Road, Xi’an, 710054, China [email protected]

ABSTRACT

Groundwater quality is basic for groundwater pollution control and remediation. The study

presented a quality assessment of phreatic water. Twenty samples were collected from the

Yinchuan Plain and ten indices were selected for the comprehensive water quality

assessment. TOPSIS method and osculating value method were applied. The study shows that

the phreatic groundwater in the Yinchuan Plain has been polluted with F-, TH, TDS, SO4

2-

and NO2-. The water should be properly treated before used for drinking. The two methods

are applicable in water quality assessment. The OVM is stricter than the TOPSIS method in

water quality assessment.

Keywords: TOPSIS, entropy weight, osculating value, water quality assessment.

1. Introduction

Groundwater, as an important natural resource, is vital to all lives on earth, especially, to

human beings. As the global population are increasing and the groundwater is becoming

more and more precious. However, with the development of society and economy,

groundwater is under the pressure of being polluted. The groundwater pollution has become

an international issue which attracts international attention. On the 24 August 2011, the

Chinese government has passed the National Groundwater Pollution Control Programs for

the period from 2011to 2020, which has brought a new era for groundwater resources

protection in China.

Groundwater quality assessment is the basis for groundwater pollution control and

remediation. Many scholars around the globe have paid their attention to this field. For

example, Baba and Tayfur (2011) identified the groundwater pollution in Turkey and the

effects of polluted groundwater on human health. Choi and Lee (2011) discussed the natural

attenuation capacity of a petroleum contaminated groundwater at a military facility in Korea.

Hamzaoui-Azaza et al. (2011) conducted a hydrochemical and statistical investigation,

discussed the sources of dissolved ions and assessed the chemical quality of the groundwater

in Zeuss–Koutine aquifer, southeastern Tunisia. Wu et al. (2010) and Li et al. (2010a) carried

out water quality assessments in Jingyuan and Pengyang County, China, respectively. Their

studies showed that the water quality in the two counties was in general good except for some

areas. The water quality in the two regions was influenced by hydrogeological conditions and

anthropogenic activities.

The aims of the present work are 1) to assess the water quality in the Yinchuan Plain with

two different multi-criteria decision making methods and 2) to compare the two multi-criteria

decision making methods. This study is essential for the groundwater pollution identification,

groundwater pollution control and groundwater protection.

Groundwater quality assessment with two multi-criteria decision making methods

LI Peiyue, WU Jianhua and QIAN Hui

International Journal of Geomatics and Geosciences

Volume 2 Issue 3, 2012 869

2. Materials and Methods

2.1 Study area

The study area is part of the Yinchuan Plain which is a fault formed basin during the

Quaternary. The Yinchuan Plain is a traditional agricultural region where the second largest

river of China, the Yellow River, runs through the plain along the east border. The Yellow

River water is mainly diverted for irrigation, but domestic water is predominately dependent

on groundwater. It is said that there would have no the plain if there were no water, and this

is true. Water plays an important role in promoting the regional economy and keeping the

society stability. The selected study area is located in the north of the plain (Figure 1).

Figure 1: Location of the study area

2.2 Data collection

Twenty groundwater samples were collected from the phreatic aquifer of the Yinchuan Plain

which is a traditional agricultural farming area. To guarantee the consistency and reliability

of the sample analysis, the standard procedures for sample collection, preservation, and lab

examination recommended by the Standard Examination Methods for Drinking Water were

followed. These samples were analyzed in the laboratory of the Ningxia Institute of Land and

Resources Investigation and Monitoring. For each sample, eighteen indices including

carbonate, bicarbonate, chloride, sulphate, calcium, magnesium, sodium, potassium, pH,

chemical oxygen demand (COD), total dissolved solid (TDS), total hardness (TH), nitrate,

nitrite, ammonia nitrogen (NH4+), fluoride (F

-) and total iron (Tfe) were analyzed and among

them ten major indices (TDS, TH, Cl-, SO4

2-, NO3

-, NO2

-, NH4

+, Tfe, F

- and CODMn) were

selected for the comprehensive water quality assessment.





2.3 TOPSIS method

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is one of

the most popular multiple criteria decision making methods and has been incorporated with

fuzzy theory and many other theories and widely used in various fields. It was first

introduced by Hwang and Yoon (1981). The procedures of TOPSIS in water quality have

been introduced in detail by Li et al. (2011a, b) and they are summarized as follows:

1. Procedure 1: Constructing the initial decision matrix according to the observed water

quality data

2. Procedure 2: Normalizing the initial decision matrix to eliminate the effects of complex

relations

3. Procedure 3: Determining the weight of each index

4. Procedure 4: Determining the positive and negative ideal reference points

5. Procedure 5: Calculating the distances to the positive and negative ideal reference points

using Euclidean distance

6. Procedure 6: Calculating the closeness coefficient (CC) of each sample and performing

the water quality assessment

2.4 Osculating value method

Osculating value method (OVM) is another multiple objective decision making optimization

method which determines the water quality by the osculating values ordering of different

water samples and the quality standards of different ranks. The water quality is determined by

the ordering of the Euclidean distance (E). Li et al. (2010b) have discussed its application to

water quality assessment and the steps are generally similar to the TOPSIS. The steps are

shown in Figure 2.

Figure 2: Flow chart of OVM in water quality assessment

2.5 Determination of Weight

In the present study, information entropy theory was used to determine the weight of each

index. It is calculated as follows (Li et al. 2010b, 2011b):

For a given set of water quality data, the eigenvalue matrix X can be constructed as follows:





11 12 1

21 22 2

1 2

n

n

m m mn

x x x

x x xX

x x x

=

L

L

M M O M

L

(1)

Where, m is the total numbers of samples, (i=1, 2,…, m). Each sample has n evaluated

parameters (j=1, 2,…, n).

For the efficiency type, the construction function of normalization is:

min

max min

( )

( ) ( )

ij ij

ij

ij ij

x xy

x x

−=

−

(2)

While for the cost type, the construction function of normalization is:

max

max min

( )

( ) ( )

ij ij

ij

ij ij

x xy

x x

−

=

−

(3)

After transform, the standard-grade matrix Y can be obtained and shown below:

11 12 1

21 22 2

1 2

n

n

m m mn

y y y

y y yY

y y y

=

L

L

M M O M

L

(4)

Then the ratio of index value of the j index and in i sample is:

∑=

=

m

i

ijijij yyP1

/ (5)

Because the Pij calculated by formula (5) may get a zero which will be nonsense in the

following calculation. Therefore, a revised form of formula (5) proposed by Zhang and Ren

(2011) was used in the study. They have proved the effectiveness of the modification.

1

( 0.0001) / ( 0.0001)m

ij ij ij

i

P y y=

= + +∑ (6)

The information entropy is expressed by the formula below:

1

1ln

ln

m

j ij ij

i

e P Pm

=

= − ∑ (7)

The smaller the value of ej is, the bigger the effect of j index. Then the entropy weight can be

calculated with the below formula:

1

1

(1 )

j

j n

j

j

e

e

ω

=

−

=

−∑ (8)





In the formula, ωj is defined as the entropy weight of jth parameter.

3. Results and Discussion

3.1 Hydrochemical characteristics

The statistical analysis results of physiochemical indices are shown in Table 1.

Table 1: Physiochemical Analysis Results of Samples

Items TH TDS Na+ K

+ Mg

2+ Ca

2+ Cl

- SO4

2-

N 20 20 20 20 20 20 20 20

Min 106.1 420 83 1.23 10 26.1 70 55.8

Max 1201.1 3162 618 20.7 194.4 160.3 573.4 1661.4

Mean 584.75 1239.85 205.95 4.52 84.72 96.77 171.63 517.18

SD 85336.02 414215.29 23066.26 22.86 2907.96 1312.08 14862.05 127952.94

SL 450 1000 — — — — 250 250

n 16 11 3 15

Items HCO3- CO3

2- NO3

- NO2

- NH4

+ Tfe F

- CODMn

N 20 20 20 20 20 20 20 20

Min 32.2 9 1 0 0 0.01 0.44 0.45

Max 384.4 64 24.6 0.65 17.8 0.39 25.2 19.1

Mean 267.66 26.2 6.65 0.1 1.06 0.07 3.36 2.42

SD 6537.35 190.69 51.06 0.03 15.81 0.01 28.73 17.19

SL — — 20 0.02 0.2 0.3 1 3

n 2 10 4 1 19 3

Note: N is the total numbers of samples, Min denotes the minimum value of an index, Max

represents the maximum value of an index, Mean is the mean value of an index, SD is the

standard deviation, SL is the maximum acceptable limit of a index in the national standard, n

is the numbers of samples exceeding the acceptable limit of the standard and — denotes there

is no such limit in the standard

The groundwater in the study area is partially polluted. Of the 20 water samples, 19 samples

are with F- exceeding the acceptable limit of the national standard, 16 samples are with

unacceptable TH and 15 with SO42-

. The TDS and NO2- are also beyond the standard limits

significantly. Pollution can also be observed in other indices. The highest concentration of

TH is 1201.1 mg/L and the lowest is 106.1 mg/L. The distributions of TH, TDS, F-, SO4

2- and

NO2- are illustrated in Figures 3 to 7, respectively. It can be seen form the Figure 3 that the

TH increases from south to north. The acceptable concentration of TH in drinking water is

450 mg/L and the figure shows that approximately 2/3 of the study area is polluted with

regard to TH. The Table 1 shows that the maximum of TDS is 3162 mg/L and the minimum

is 420 mg/L with the average of 1239.85 mg/L. The statistical analysis shows that the

groundwater is seriously polluted with respect to TDS. It can also be observed from the

Figure 4 that over half of the study area is high with TDS. The TDS shows a decrease trend

from northeast and southeast to the middle.





Figure 3: Distribution of TH in the area

Figure 4: Contours of TDS in the study area





Figure 5: Spatial distribution of F-

Figure 6: Distribution of SO42-





Figrue 7: Spatial variation of NO2-

High fluoride concentration water is particularly disastrous to human. The acceptable limit of

F- in drinking water is 1.0 mg/L in the national drinking water standard. The statistical

analysis shows that the highest concentration of F- is 25.2 mg/L and the lowest is 0.44 mg/L,

and 19 of the 20 samples exceeded the permissible limit for F−. The F

- concentration (Figure

5) shows an increase trend from the east to the west and over 80% of the whole study area

has been polluted by F-. The SO4

2- and NO2

- pollution are also serious in the study area. It can

be seen from the Figures 6 and 7 that the SO42-

concentration increases from south and north

and from west to east, and the NO2- concentration increases gradually from the surrounding

areas to the middle.

It can be concluded from above analysis that the groundwater in the study area has been

polluted. The groundwater is already not fit for direct human consumption. If the

groundwater is used for drinking, some necessary measures should be taken before water

supply. However, the suitability for irrigation use was not assessed in the paper. Therefore,

further assessment for irrigation purpose is required.

3.2 Water quality assessment

The water quality was assessed with the methods introduced above and the results are shown

in Table 2.

Of the 20 samples, only one sample is good quality water, and most of the water samples are

fair quality which is generally fit for drinking, but before consumption, some necessary

measures should be taken to ensure the indices are all within the acceptable limits. Otherwise,

some human health problems may be caused by the water. Some samples are poor quality and

some are even polluted. These samples are not fit for human drinking. Generally speaking,

Fair quality water is fit for drinking with proper pretreatment, poor quality water can be used

for irrigation and polluted water must be treated before used.





Table 2: Water Quality Assessment Results

Sample

No. E

Water quality

of OVM

Quality

description CC

Water quality

of TOPSIS

Quality

description

W01 4.64 III Fair 0.86 III Fair

W02 8.95 IV Poor 0.76 IV Poor





W07 6.61 IV Poor 0.81 III Fair

W08 6.44 IV Poor 0.81 III Fair






W14 18.88 V Polluted 0.57 IV Poor



W17 0.67 II Good 0.95 II Good

W18 8.56 IV Fair 0.77 III Fair


W20 11.92 IV Fair 0.70 III Fair

The assessment results by the two methods are a little different. For example, sample W07

and W08 are poor quality waters based on OVM, but are fair quality waters by TOPSIS

method. Sample W14 is polluted water according to OVM, but poor quality water by TOPSIS.

Theses differences show that the OVM is stricter than the TOPSIS method in water quality

assessment. The stricter assessment will ensure the human health but will reduce the total

available volumes of groundwater. Most of the assessment results by the two methods are the

same, which shows that the two methods can both be applied to water quality assessment.

4. Conclusions

OVM and TOPSIS were used for the comprehensive water quality assessment. The following

conclusions are reached.

1. The phreatic groundwater in the Yinchuan Plain has been seriously polluted with

respect to F-, TH, TDS, SO4

2- and NO2

-. Other indices are also high in content. The

comprehensive assessment shows most of the samples are fair quality waters and can

be used for drinking with proper treatment. Other poor quality water can be used for

irrigation and polluted water should be treated before used.





2. The two methods (OVM and TOPSIS) are both applicable in water quality

assessment, although their assessment results are a little different. The OVM is

stricter than the TOPSIS method in water quality assessment.

Acknowledgements

The research was supported by the Doctor Postgraduate Technical Project of Chang’an

University (CHD2011ZY025 and CHD2011ZY022), the Special Fund for Basic Scientific

Research of Central Colleges (CHD2011ZY020) and the National Natural Science

Foundation of China (40772160 and 41172212). The anonymous reviewers and the editor are

greatly acknowledged for their useful comments on the paper.

5. References

1. Baba A and Tayfur G., (2011), Groundwater contamination and its effect on health in

Turkey, Environmental Monitoring and Assessment, 83, pp 77–94.

2. Choi HM and Lee JY., (2011), Groundwater contamination and natural attenuation

capacity at a petroleum spilled facility in Korea, Journal of Environmental Sciences,

23(10), pp 1650-1659.

3. Hamzaoui-Azaza F, Ketata M., Bouhlila R., Gueddari M. and Riberio L., (2011),

Hydrogeochemical characteristics and assessment of drinking water quality in Zeuss–

Koutine aquifer, southeastern Tunisia, Environmental Monitoring and Assessment,

174, pp 283-298.

4. Wu JH, Li PY and Qian H., (2011), Groundwater Quality in Jingyuan County, a

Semi-Humid Area in Northwest China, E-Journal of Chemistry, 8(2), pp 787-793.

5. Li PY, Qian H and Wu JH., (2010a), Groundwater quality assessment based on

improved water quality index in Pengyang County, Ningxia, Northwest China, E-

Journal of Chemistry, 7(S1), pp S209-S216.

6. Hwang CL and Yoon K., (1981), Multiple attribute decision making methods and

applications. Berlin: Springer–Heidelberg.

7. Li PY, Qian H and Wu J H., (2011a), Hydrochemical Formation Mechanisms and

Quality Assessment of Groundwater with Improved TOPSIS Method in Pengyang

County Northwest China, E-Journal of Chemistry, 8(3), pp 1164-1173.

8. Li PY, Wu JH and Qian H., (2011b), Groundwater quality assessment based on rough

sets attribute reduction and TOPSIS method in a semi-arid area, China,

Environmental Monitoring and Assessment, doi: 10.1007/s10661-011-2306-1.

9. Li PY, Wu JH and Qian H., (2010b), Groundwater quality assessment based on

entropy weighted osculating value method, International Journal of Environmental

Sciences, 1(4), pp 621-630.

10. Zhang JL and Ren J., (2011), The Deficiencies and Amendments of the Calculation

Formulate of Entropy and Entropy Weight in the Theory of Entropy. Statistics &

Information Forum, 26(1), pp 1-5.

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