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Environ Monit Assess (2010) 160:215227

DOI 10.1007/s10661-008-0689-4

Groundwater quality mapping in urban

groundwater using GISBilgehan Nas Ali Berktay

Received: 19 September 2008 / Accepted: 20 November 2008 / Published online: 19 December 2008 Springer Science + Business Media B.V. 2008

Abstract Konya City, located in the central part

of Turkey, has grown and urbanized rapidly. A

large amount of the water requirement of Konya

City is supplied from groundwater. The quality

of this groundwater was determined by taking

samples from 177 of the wells within the study

area. The purposes of this investigation were (1)

to provide an overview of present groundwater

quality and (2) to determine spatial distribution

of groundwater quality parameters such as pH,

electrical conductivity, Cl, SO42, hardness, andNO3

concentrations, and (3) to map groundwa-

ter quality in the study area by using GIS and

Geostatistics techniques. ArcGIS 9.0 and ArcGIS

Geostatistical Analyst were used for generation

of various thematic maps and ArcGIS Spatial

Analyst to produce the final groundwater quality

map. An interpolation technique, ordinary krig-

ing, was used to obtain the spatial distribution of

groundwater quality parameters. The final map

shows that the southwest of the city has optimum

groundwater quality, and, in general, the ground-water quality decreases south to north of the city;

5.03% (21.51 km2)of the total study area is clas-

sified to be at the optimum groundwater quality

level.

B. Nas (B) A. BerktayDepartment of Environmental Engineering,Selcuk University, 42075, Konya, Turkeye-mail: [email protected]

Keywords GIS Geostatistics

Groundwater quality Kriging

Introduction

Groundwater is an important source of drinking

water for many people around the world, espe-

cially in rural areas. Groundwater can become

contaminated from natural sources or numerous

types of human activities. Residential, municipal,commercial, industrial, and agricultural activities

can all affect groundwater quality. Contamination

of groundwater can result in poor drinking water

quality, loss of water supply, high cleanup costs,

high costs for alternative water supplies, and/or

potential health problems.

Natural resources and environmental con-

cerns, including groundwater, have benefited

greatly from the use of GIS. Typical examples

of GIS applications in groundwater studies are

site suitability analyses, managing site inventory

data, estimating vulnerability of groundwater to

pollution potential from nonpoint sources of pol-

lution, modeling groundwater movement, model-

ing solute transport and leaching, and integrating

groundwater quality assessment models with spa-

tial data to create spatial decision support systems

(Engel et al. 1999). Hudak (1999, 2000, 2001)

and Hudak and Sanmanee (2003) have reported

a number of studies about Texas groundwater

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216 Environ Monit Assess (2010) 160:215227

quality. ArcView GIS was used to map, query,

and analyze the data in these studies. Vinten and

Dunn (2001) studied the effects of land use on

temporal changes in well water quality. Levallois

et al. (1998) studied groundwater contamination

through nitrates associated with intense potato

culturing in Qubec, Canada. The data analysiswas carried out by combining GIS and statisti-

cal methods. Ahn and Chon (1999) investigated

groundwater contamination and spatial relation-

ships among groundwater quality, topography, ge-

ology, land use, and pollution sources using GIS in

Seoul, Korea. Ducci (1999) produced groundwa-

ter contamination risk and quality maps by using

GIS in Italy. Fritch et al. (2000) developed an

approach to evaluate the susceptibility of ground-

water in north-central Texas to contamination.

Interpolation is the estimation ofZvalues of asurface at an unsampled point based on the knownZ values of surrounding points. There are two

main groupings of interpolation techniques: deter-

ministic and geostatistical. Deterministic interpo-

lation techniques create surfaces from measured

points, based on either the extent of similarity

(e.g., inverse distance weighted (IDW)) or the

degree of smoothing (e.g., radial basis functions).

Geostatistical interpolation techniques (e.g., krig-

ing) utilize the statistical properties of the mea-

sured points (ESRI (Environmental SystemsResearch Institute)2001).

Applications of geostatistics can be found in

very different disciplines ranging from the classi-

cal fields of mining and geology to soil science,

hydrology, meteorology, environmental sciences,

agriculture, and even structural engineering. Krig-

ing is used widely in geology, hydrology, environ-

mental monitoring, and other fields to interpolate

spatial data (Stein 1999). With the recent advances

in computation facilities and the availability of

geostatistical software, the use of kriging in the

spatial analysis of environmental data has become

increasingly popular. Today, a number of variants

of kriging are in general use, and these are simple

kriging, ordinary kriging (OK), universal kriging,

block kriging, cokriging, and disjunctive kriging.

Among the various forms of kriging, OK has

been used widely as a reliable estimation method

(Yamamoto2000). Kriging is distinguished from

IDW and other interpolation methods by tak-

ing into consideration the variance of estimated

parameters (Buttner et al. 1998). OK is most

commonly adopted for environmental studies

(Poon et al.2000; Kravchenko and Bullock1999;

Lin et al. 2001; Tranchant and Vincent 2000;

Gringarten and Deutsch 2001). A more detailed

explanation of the kriging method is given byStein (1999), Yamamoto (2000), Gringarten and

Deutsch (2001), Mcgrath and Zhang (2003), and

Cressie (1990). Zimmerman et al. (1999) com-

pared the accuracy of OK, Universal Kriging, and

IDW methods based on an analysis of synthetic

data. Pozdnyakova and Zhang (1999) used the

geostatistical methods of kriging and cokriging

to estimate the sodium adsorption ratio in an

agricultural field. Zhu et al. (2001) produced a

radon distribution map using the kriging and GIS

techniques in Belgium. Dagostino et al. (1998)investigated the spatial distribution of nitrate con-

centration in the aquifer of central Italy and com-

pared cokriging and OK techniques.

The purposes of this investigation are (1) to

provide an overview of present groundwater qual-

ity and (2) to determine spatial distribution of

groundwater quality parameters such as pH, elec-

trical conductivity, chloride, sulfate, hardness, and

nitrate concentrations, and (3) to map groundwa-

ter quality in the Konya City area by using GIS

and geostatistics techniques.

Description of study area

The city of Konya is located at between 36.5 and

39.5 north latitude and 31.534.5 east longitude

and is the largest province of Turkey with a sur-

face area of 38,183 km2. The population of the city

is about 850,000. Figure 1 shows the location of

Konya City. Study area has about 17.1 km wide

from east to west and 25 km long from north to

south, which yields a total area of 427.5 km2.

A large proportion of water requirements for

the City of Konya are supplied from 198 ground-

water wells. Presently, new deep wells are still be-

ing drilled and operated by the Water Authority

of Konya City Municipality (WAKCM), as the

water requirements of the city constantly increase.

Depth of the wells varies between 25 m (mini-

mum) and 206 m (maximum), with an average of

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Fig. 3 A simplifiedgeological map showingsampling points anddirection of groundwaterflow

Neogene aged limestones and sandygravelly lev-

els of Plio-Quaternary aged detritus.

Land use pattern

Figure 4 shows the land use patterns of study area.

Konya City is divided into five (development ar-

eas, residential areas, industrial areas, agricultural

areas, and cemeteries) districts. The locations of

the 177 groundwater wells were classified accord-

ing to land use patterns: 82 samples in residen-

tial district, 30 samples in development district,

13 samples in industrial district, 27 samples in

agricultural district, one sample in cemetery, and

23 samples other areas. There are 60 cemeter-

ies actively used within the city area. Location

and distances of the cemeteries from water wells

are illustrated in the generalized land uses map

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Environ Monit Assess (2010) 160:215227 219

Fig. 4 Land use patternof Konya City

in Fig. 4. Twenty-seven water wells are located

south of the city. Some vegetables such as carrot,

tomato, green pepper, cucumber, etc. are culti-

vated, and nitrogen-based fertilizers are used by

farmers in this area. Eighty-two water wells are

located in the residential area of the city. In this

area, new residential and industrial activities have

been rapidly increasing over the years.

Materials and methods

A GIS software package ArcGIS 9.0 and Arc-

GIS Geostatistical Analyst extension were used

to map, query, and analyze the data in this study

for the assessment of groundwater quality. The

paper map of the city has a 1:25,000 scale and

was digitized to UTM coordinate system (6 sec-

tion width) by applying the on-screen digitizing

method. The well locations were obtained for 177

wells spreading all over the region by using a

Magellan Spor Trak hand held Global Positioning

System (GPS) receiver. In addition, attribute in-

formation of wells was also input to a digital map

using the ArcGIS 9.0 software.

Chemical analysis

Groundwater samples were taken directly from

177 wells in April and May 2001 by the WAKCM.

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The wells were pumped until the temperature-,

conductivity-, and pH-stabilized. Glass containers

were used for the collection of water samples for

the analyses and delivered to the WAKCM labo-

ratory within 2 h. Analyses were normally carried

out as soon as the samples reach the laboratory.

Water quality parameters (chloride, sulfate,hardness) were then analyzed in the laboratory

according to the methods given in the Standard

Methods (APHA, AWWA, WPCF 1985). Sam-

ple pH was measured using a glass electrode pH

meter. Electrical conductivity was measured using

a platinum electrode conductivity meter. Nitrate

(NO3) concentrations were measured with a UV

VIS spectrophotometer by Brucine colorimetric

method (APHA, AWWA, WPCF1976).

Spatial interpolation of groundwaterquality parameters

In this study, a geostatistical software package

called ArcGIS Geostatistical Analyst Extension

was used for the ordinary kriging estimations.

Ordinary kriging was used to obtain the spatial

distribution of groundwater quality parameters

over the area. The groundwater quality data has

been checked by a histogram tool and normal

QQPlots to see if it shows a normal distribution

pattern. For each water quality parameter, ananalysis trend was made and the 11 different semi-

variogram models were tested. Prediction perfor-

mances were assessed by cross-validation.

Results and discussion

Groundwater quality gives a clear picture about

the usability of the water for different purposes.

The standard quality for drinking water has

been specified by the World Health Organization

(WHO) and the Turkish Standard Institute (TSE;

(WHO) World Health Organization 2004; TSE

1997). It has given the permissible and desir-

able limits for the presence of various elements

in groundwater (Table 1). Statistical evaluation

of groundwater quality parameters can be seen

in Tables 2 and 3 shows chemical composi-tions of groundwater relating to land use in the

Konya City.

Examining the distribution of the data

Kriging methods work best if the data are ap-

proximately normally distributed. Transforma-

tions were used to make data normally distributed

and satisfy the assumption of equal variability for

the data. In the ArcGIS Geostatistical Analyst,

the histogram and normal QQPlots were used tosee what transformations, if any, are needed to

make the data more normally distributed.

Normal QQPlots provide an indication of uni-

variate normality. If the data is asymmetric (i.e.,

far from normal), the points will deviate from the

line. Histogram tool and normal QQPlots analysis

were applied for each water quality parameter,

and it was found that only the pH parameter

showed a normal distribution. It was determined

that electrical conductivity, chloride, sulfate, hard-

ness, and nitrate concentrations do not show nor-mal distributions. For those parameters, a log

transformation has been applied to make the dis-

tribution closer to normal.

Examining the global trend through

trend analysis

The trend tool raises the points above a plot of

the study site to the height of the values of the

attribute of interest in a three-dimensional plot of

the study area. The points are then projected in

Table 1 Standards forquality of drinkingwater (TSE TurkishStandar Institute1997;WHO World HealthOrganization1985)

MCLmaximumcontaminant level

Parameter WHO (2004) TSE (1997) TSE (1997)

MCL recommend MCL

pH 6.58.5 6.58.5 6.59.2

Conductivity (S/cm) 400 2,000

Chloride (mg/L) 250 25 600

Sulfate (mg/L) 250 25 250

Hardness (F) 50

Nitrate (mg/L) 50 25 50

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Table 2 Statistical evaluation of groundwater quality parameters (n = number of studied samples)

pH n Conductivity n Chloride n Sulfate n Hardness n Nitrate n

(S/cm) (mg/L) (mg/L) (F) (mg/L)

8 13 > 2, 000 1 > 100 2 > 100 5 > 50 19 > 50 3

Parameter Min. Max. Mean Median SD Skewness Kurtosis

pH 7.0 8.6 7.59 7.6 0.28 0.64 3.73

Conductivity (S/cm) 332 2,892 680.4 608 305.32 3.08 18.61

Chloride (mg/L) 10 520 44.7 28 49.17 5.85 52.03

Sulfate (mg/L) 7 550 64.6 37 75.01 3.54 19.04

Hardness (F) 15 86 36.4 34 11.72 1.42 6.15

Nitrate (mg/L) 3 110 16.1 14 13.66 4.27 26.07

two directions onto planes that are perpendicular

to the map plane. A polynomial curve is fit to

each projection. If the curve through the projected

points is flat, no global trend exists. In this situa-

tion, it is not necessary to remove the trend before

modeling the semivariogram/covariance for krig-

ing. For each water quality parameter, an analysis

trend was made, and it was determined that there

is no global trend for all parameters.

Semivariogram models

In this study, the semivariogram models (Cir-

cular, Spherical, Tetraspherical, Pentaspherical,

Exponential, Gaussian, Rational Quadratic, Hole

effect, K-Bessel, J-Bessel, Stable) were tested

for each parameter data set. Prediction perfor-

mances were assessed by cross-validation. Cross-

validation allows determination of which model

provides the best predictions.

For a model that provides accurate predictions,

the standardized mean error should be close to 0,

the root-mean-square error and average standard

error should be as small as possible (this is use-

ful when comparing models), and the root-mean

square standardized error should be close to 1.

When the average estimated prediction standard

errors are close to the root-mean-square predic-

tion errors from cross-validation, then you can

be confident that the prediction standard errors

are appropriate (ESRI (Environmental Systems

Research Institute)2001).

Table 3 Chemical compositions of groundwater relating to land use

Land use pH Conductivity Chloride Sulfate Hardness Nitrate

(S/cm) (mg/L) (mg/L) (F) (mg/L)

Development areas (30) Ave. 7.7 530 27.8 48 28.9 13.5

Max. 8.6 1,448 115 210 59 26

Min. 7.1 332 12 7 15 3.7

Residential areas (82) Ave. 7.6 725.6 50.3 78 37.9 18.9

Max. 8.3 1,612 238 550 77 110

Min. 7.9 418 15 16 15 3

Cemetery areas (1) 7.3 1,542 160 110 72 84

Industrial areas (13) Ave. 7.4 1,009 106.5 111 45.5 12.2

Max. 8.1 2,892 520 220 86 22

Min. 7.3 452 25 26 22 8

Agricultural areas (27) Ave. 7.7 532.4 23.2 21.3 32.9 9.84

Max. 8.1 938 50 50 55 23

Min. 7 377 13 8 22 3

Others (23) Ave. 7.6 678 32.8 62.3 37.7 15.3

Max. 8.1 1,700 140 450 78 28

Min. 7 412 10 8 20 5

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After applying different models for each water

quality parameter examined in this study, the er-

ror was calculated using cross-validation and mod-

els giving best results were determined. Table 4

shows the most suitable models and their pre-

diction error values for each parameter. Table 4

also shows that for different parameters, differentmodels may give better results.

The groundwater quality map

Figure5shows the spatial distribution of pH, con-

ductivity, chloride, sulfate, hardness, and nitrate

concentrations in study area, respectively.

A groundwater quality map (Fig. 6) was created

following the classification shown in Table5. The

construction of the groundwater quality map wascarried out through the overlapping of the the-

matic maps (Fig.5af), which are produced as a

result of kriging interpolations. The spatial inte-

gration for final water quality mapping was carried

out using ArcGIS Spatial Analyst extension.

pH

No health-based guideline value is proposed for

pH. Although pH usually has no direct impact on

consumers, it is one of the most important opera-tional water quality parameters, with the optimum

pH required often being in the range of 6.59.5

(WHO (World Health Organization)2004).

The maximum contaminant level (MCL) for

pH in drinking water is given as to be 6.58.5 mg/L

by the WHO but 6.59.2 by the TSE. In addi-

tion, Turkish Standards recommend the value of

6.58.5 for pH (WHO (World Health Organiza-

tion)2004; TSE1997).

The minimum and maximum values of pH were

measured as 7.0 and 8.6, respectively. There was

no well in which the pH exceeds the MCL of 9.2

given in the Turkish Standards. Spatial distribu-

tions of pH concentrations are shown in Fig. 5a.It is shown that the low pH concentrations (7.0

7.5) occur north-east of the city and within the city

center.

Conductivity

Electrical conductivity (EC) is a parameter re-

lated to total dissolved solids (TDS). The impor-

tance of EC and TDS lies in their effect on the

corrrosivity of a water sample and in their effecton the solubility of slightly soluble compounds

such as CaCO3. TDS is comprised of inorganic

salts (principally calcium, magnesium, potassium,

sodium, bicarbonates, chlorides, and sulfates) and

small amounts of organic matter that are dissolved

in water. Concentrations of TDS in water vary

considerably in different geological regions ow-

ing to differences in the solubilities of minerals

(WHO (World Health Organization)2004).

For EC, the value of 400 S/cm is rec-

ommended with the MCL of 2,000 S/cm byTurkish Standards (1997). The recommended

value of 400 S/cm was obtained from 11 out

of 177 wells. The MCL of 2,000 S/cm was

only exceeded in well No. 54 with the value of

2,892 S/cm. As shown in Fig. 5b, the EC value in-

creases from south to north with the upper ranges

being greater than 1,000 S/cm.

Table 4 Fitted parameters of the theoretical variogram model for groundwater quality parameters

Parameters Models Prediction errors

Mean Root-mean Average standard Mean Root-mean-square

square error standardized standardized

pH Stable 0.00207 0.2239 0.2252 0.01019 1.0

Conductivity Tetraspherical 11.67 258 227.6 0.03142 1.004

Chloride Stable 2.325 42.75 33.4 0.04598 1.163

Sulfate Rational quadratic 2.876 61.13 91.95 0.02767 0.8174

Hardness Gaussian 0.2076 9.439 9.21 0.008576 0.9892

Nitrate K-Bessel 0.3677 12.18 9.844 0.02927 1.093

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a

d e f

b c

Fig. 5 Spatial distribution ofa pH,belectrical conductivity,c chloride,d sulfate,e hardness, andfnitrate concentrations

Chloride

Chlorides occur in all natural waters in widely

varying concentrations. The chloride content nor-

mally increases as the mineral content increases

(Sawyer and Mccarty1978). The chloride ion oc-

curs in natural waters in fairly low concentrations,

usually less than 100 mg/L, unless the water is

brackish or saline (Fetter1999). No health-based

guideline value is proposed for chloride in drink-

ing water. High concentrations of chloride give a

salty taste to water and beverages (WHO (World

Health Organization) 2004). However, chloride

concentrations in excess of about 250 mg/L can

give rise to a detectable taste in water (WHO

(World Health Organization) 2004; Sawyer and

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Fig. 6 Final groundwater quality map

Mccarty 1978). Chloride in drinking water orig-

inates from natural sources, sewage and indus-

trial effluents, urban runoff containing de-icing

salt, and saline intrusion (WHO (World Health

Organization)2004).

The MCL for chloride in drinking water is

given as 250 mg/L by the WHO but 600 mg/L by

the TSE. In addition, Turkish standards recom-mend the value of 25 mg/L for chloride (WHO

(World Health Organization) 2004; TSE 1997).

Chloride concentration was complied with a value

of 25 mg/L for 52 out of 177 wells. There was

no water well in which the chloride concentration

exceeds the MCL given in Turkish Standards.

As indicated by Fig.5c, chloride concentration

increased from southwest to northeast. In a wide

area around the south and west part of the city,

less than 50 mg/L chloride concentration occurs.

Sulfate

Sulfates occur naturally in numerous minerals and

are used commercially, principally in the chem-

ical industry. They are discharged into water in

industrial wastes and through atmospheric depo-

sition; however, the highest levels usually occur

in groundwater and are from natural sources. No

health-based guideline is proposed for sulfate.Sulfur is widely present in reduced forms in ig-

neous, sedimentary, and metamorphic rocks as

metallic sulfides. This sulfur turns to sulfate when

weathered in contact with aerated water. Water

in igneous or metamorphic rocks generally con-

tains less than 100 mg/L sulfate, but sedimentary

rocks can contain much higher levels (Fytianos

and Christophoridis 2004). The presence of sulfate

in drinking water can cause a noticeable taste,

and very high levels might cause a laxative effect

in unaccustomed consumers. Taste impairmentvaries with the nature of the associated cation;

taste thresholds have been found to range from

250 mg/L for sodium sulfate to 1,000 mg/L for cal-

cium sulfate (WHO (World Health Organization)

2004).

TSE and WHO indicate the MCL of 250 mg/L

for sulfate. A concentration of 25 mg/L is rec-

ommended in Turkish Standards (WHO (World

Health Organization) 2004; TSE 1997). The sul-

fate concentration of 25 mg/L can be seen in 36 of

the 177 wells. But, the MCL of sulfate (250 mg/L)was exceeded in wells No. 72, No. 81, No. 64,

No. 34, and No. 37. It can be seen in Fig. 5d that

the sulfate concentration increases from south to

Table 5 Groundwater quality classification (Ducci1999)

Quality Class Chloride (mg/L) Sulfate (mg/L) Hardness (F) Conductivity (S/cm) Nitrate (mg/L)

Optimum A < 50 < 50 < 30 < 1,000 < 10

Medium B 50200 50250 3050 1,0002,000 1050

Poor C > 200 > 250 > 50 > 2,000 > 50

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north of the city and it exceeds MCL of 250 mg/L

northwest of the city.

Hardness

Hardness in water is caused by dissolved calciumand, to a lesser extent, magnesium. It is usually

expressed as the equivalent quantity of calcium

carbonate (WHO (World Health Organization)

2004). The hardness of water reflects the nature

of the geological formations with which it has

been in contact (Sawyer and Mccarty 1978). A

number of ecological and analytical epidemio-

logical studies have shown a statistically signifi-

cant inverse relationship between the hardness of

drinking water and cardiovascular disease. There

is some indication that very soft waters may havean adverse effect on mineral balance, but detailed

studies were not available for evaluation. Public

acceptability of the degree of hardness may vary

considerably from one community to another de-

pending on local conditions, and the taste of water

with hardness in excess of 500 mg/L is tolerated by

consumers in some instances. Soft water, with a

hardness of less than 100 mg/L, may, on the other

hand, has a low buffering capacity and so can

be more corrosive for water pipes (WHO (World

Health Organization)2004).There is no limitation of water hardness by

Turkish Standards but the WHO gives a value of

50F as the maximum value for water hardness

(WHO (World Health Organization) 2004). There

is no water hardness value at all between 7.5

15F and 07.5F, which are classified as mod-

erately hard and soft water in the study area,

respectively. The minimum, maximum, and mean

values of hardness are 15F, 86F, and 36.4F,

respectively.

The north of the study area has an aquifer

lithology of limestone and the hardness value in

this area reaches up to 50F. Insoluble bicarbon-

ates are converted to soluble carbonates because

of the existence of carbon dioxide in the soil.

Since limestone is not pure carbonate but includes

impurities such as sulfates and chlorides, these

materials become exposed to the solvent action of

the water as the carbonates are dissolved and they

pass into solution, too. Therefore, chlorides and

sulfate concentrations as well as water hardness

were very high in such areas. There was no water

well on the aquifer lithology of the limestone

(Neogene), limestone (Paleozoic), and clay (Plio-

Quaternary) in the study area (Fig.3).

The south of the study area (Alakova region)

has sandy, gravelly (Plio-Quaternary) and sandyclay (Plio-Quaternary) aquifer lithology, and the

hardness value in this area was observed to be

between 30F and 50F. From the north of the

Alakova region to southwest, west and northwest

of the city, the water hardness was estimated to

be less than 30F. The value of water hardness

increases from south to northeast of the city area

(Fig.5e).

Nitrate

The nitrate concentration in groundwater and sur-

face water is normally low but can reach high

levels as a result of leaching or runoff from agri-

cultural land or contamination from human or

animal wastes as a consequence of the oxidation

of ammonia and similar sources (WHO (World

Health Organization)2004).

During recent years, the problem of ground-

water contamination by nitrates has been stud-

ied thoroughly all over the world (Hudak 1999,

2000; Vinten and Dunn 2001; Levallois et al.1998; TSE1997; Nas and Berktay2006; Fytianos

and Christophoridis 2004). The primary health

concern regarding nitrate and nitrite is the for-

mation of methemoglobinemia, the so-called blue-

baby syndrome. Several studies document adverse

effects of higher nitrate levels, most notably

methemoglobinemia (Hudak 1999, 2000; Levallois

et al. 1998; WHO (World Health Organization)

1985,2004; EPA (U.S. Environmental Protection

Agency)1993).

The MCL of nitrate is given as 50 mg/L by the

TSE and WHO for drinking water. On the other

hand, the TSE describes the limit of concentra-

tion of nitrate as 25 mg/L (WHO (World Health

Organization)2004; TSE1997).

Spatial distributions of nitrate concentrations

are shown in Fig.5f. The nitrate concentrations of

84, 95, and 110 mg/L were measured in the wells

No. 31, No. 2, and No. 41, respectively. Nitrate

concentrations for these three wells exceed the

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MCL of 50 mg/L indicated by the TSE. Ceme-

teries contribute to nitrate pollution into ground-

water (EPA (U.S. Environmental Protection

Agency) 1993). Well No. 31 was placed at the

cemetery, and this high level of nitrate could be

attributed to its location. On the other hand, well

No. 41 was placed at a park in the city centerwhere fertilizers are often applied to the lawns.

Fertilizers may cause this high level of nitrate

concentration. In the same area, a 46 mg/L nitrate

concentration was also measured on the samples

taken from well No. 33. This groundwater well

was also taken out of operation. As a result of

this investigation, wells No. 31, No. 2, and No. 41

had been taken out of operation. Nitrate contents

for 12 groundwater wells do not meet the stan-

dard of 25 mg/L indicated by the TSE, whereas

average nitrate concentration at residential ar-eas is 18.9 mg/L and in the agricultural areas is

9.84 mg/L.

Figure 6 shows the final water quality map

that was produced by overlapping of the thematic

maps as a result of geostatistical analysis. The

spatial integration for groundwater quality map-

ping was carried out using ArcGIS Spatial Analyst

extension. The area for optimum water quality

could be found on a large area at the southwest

and small areas at city center and northwest of

the city. These areas for optimum water qualitycover 5.03% (about 21.51 km2) of total study area.

The rest of the study area, which is about 94.97%

(405.99 km2), has water classified as medium and

poor quality levels. In addition, some water wells

have poor water quality in terms of nitrate con-

centration especially at the old occupied part of

the city center (Fig.5f).

Conclusions

The primary objective of this study was to map

and evaluate the groundwater quality in Konya

City. Spatial distribution of groundwater quality

parameters were carried out through GIS and

geostatistical techniques. These techniques have

successfully demonstrated its capability in ground-

water quality mapping of Konya City.

Geostatistical techniques create surfaces incor-

porating the statistical properties of the measured

data. Because geostatistics is based on statistics,

these techniques produce not only prediction sur-

faces but also errors or uncertainty surfaces, giving

you an indication of how good the predictions are.

The disadvantage of kriging is the need to make

many decisions regarding transformations, trends,

models, and parameters. However, the advantagesof this system are that it is very flexible, allows

assessment of spatial autocorrelation, and can ob-

tain prediction standard errors.

The final map, which is a groundwater quality

map, shows that the southwest of the city has

optimum groundwater quality, and, in general, the

groundwater quality decreases south to north of

the city. The deep wells in and around the city

center are located generally in Plio-Quaternary

aged soft and partially cemented sediments. On

the other hand, the south of the city is a low-density residential area and a mainly agricultural

area. Industrial activities are located northeast of

the city. Forty-six percent of wells (82 wells) are

located in the residential areas of the city. In this

area, new residential and industrial activities have

been rapidly increasing over the years.

Prior to new well drilling, the groundwater

quality map as a result of this research has to

be taken into account by WAKCM as a decision

support system.

Acknowledgements This study was supported bySelcuk University Scientific Research Fund with projectNo: 2001/145. The authors would like to thank WaterAuthority of Konya City Municipality (WAKCM).

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