Multivariate statistics in groundwater

7/30/2019 Multivariate statistics in groundwater

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Natural Resources Research, Vol. 9, No. 2, 2000

Multivariate Statistical Analysis in the Assessment ofHydrochemistry of the Northern Korinthia Prefecture

Alluvial Aquifer System (Peloponnese, Greece)

K. Voudouris,1 A. Panagopoulos2,3 and J. Koumantakis2

Received 21 December 1999; accepted 14 February 2000

The application of multivariate statistical analyses of hydrochemical data has proved to bemost successful in the assessment of groundwater hydrochemistry, especially in situationswhere numerous samples are available. Fifteen (15) hydrochemical parameters were considered(pH, E.C., T.D.S., T.H, Ca, Mg, Na, K, HCO3, Cl, SO4, NO3, NO2, NH4, PO4) in 131 samples

collected from the alluvial aquifer of NE Korinthia, during May 1997. Simple and multipleregression, factor, and trend-surface analyses were applied in order to examinethe importance ofeach parameter, investigate correlations among them, and separate them into groups. Statisticalfactors were selected and their geographical distribution was mapped. It was concluded thatuse of such methods reveal the prevailing evolutionary mechanisms of the studied system,thus enabling accurate and relatively quick hydrochemical assessments.

KEY WORDS: Hydrochemistry; hydrogeology; multivariate statistical analysis; Greece.

INTRODUCTION Koumantakis, and Stavropoulos, 1999). The regionstudied is located between N37 55 and N37 59latitude and E22 44 and E22 55 longitude.The coastal part of the northern Korinthos prefec-

ture is characterized by increasing trend in water con- Study and interpretation of groundwater chemis-try is enhanced significantly by the use of multivariatesumption, as a result of intense urbanization, touristdevelopment, and irrigated land expansion. Regional statistical analysis, especially when a large number of

samples is available. During the last decades, thesewater needs are covered mainly by groundwaterabstracted from the alluvial aquifer via numerous wells methods have been applied systematically by numer-

ous researchers in an attempt to interpret variousand boreholes and partly by winter surface runoff ofthe Asopos River, which is located on the western hydrochemical problems (Douglas and Leo, 1977;

Seyhan, Van de Griend, and Engelen, 1985; Briz-edge of the studied region (Fig. 1). Complete lack ofa rational water-resources management scheme in the Kishore and Murali, 1989; Ruiz, Gomis, and Blasco,

1990; Rao, Reddy, and Nayudu, 1996, Jayakumar andstudied alluvial aquifer system has resulted in signifi-cant groundwater quality deterioration and in the estab- Siraz, 1997, Buccianti, 1997; Voudouris and others,lishment of negative water balance (Panagopoulos, 1997; Daskalaki and Voudouris, 1997; Chen and oth-

ers, 1997; Papatheodorou and Lambrakis, 1997; Ratha

and Venkataraman, 1997).Simple and multiple regression analysis, factor1 Section of Hydrogeology, Department of Geology, University of

Patras, Patras, Greece. analysis, and trend-surface analysis were used in the2 Section of Applied Geology, Department of Mining and Metallur- framework of this study on hydrochemical data of

gical Engineering, National Technical University of Athens, Her-131 groundwater samples collected from the northern

oon Polytechniou 9, 15780, Athens, Greece.coastal alluvial aquifer system of the Korinthos Prefec-3 To whom correspondence should be addressed. (e-mail:

[email protected]) ture, southern Greece. These methods were employed

135

1520-7439/00/0600-0135$18.00/0 2000 International Association for Mathematical Geology


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136 Voudouris, Panagopoulos, Komantakis

Figure 1. Geological map study area.

in order to determine the prevailing groundwater tial deposits originating from the streamsrivers that

flow across the studied region (Fig. 1).hydrochemical evolution mechanisms on a regionalscale. Statistical analyses complemented the conven- South of the national highway, which bounds thestudy region, the Tyrrhenian deposits of coastal origintional study of simple hydrochemical maps and clari-

fied results that were occasionally ambiguous or even crop out. Usually, they consist of highly consolidatedbreccio-conglomerates, sand, small-size gravel, andcontradicting. This was a result of either local or bore-

hole scale exhibited anomalies in the measured and sporadically marl intercalations. It is believed thatthese deposits are expanding to the north locally,calculated hydrochemical parameter values or of

simultaneous existence of more than one hydrochemi- underneath the Recent unconsolidated deposits of theplain. Their outcrop is disrupted as a result of erosion.cal processes in the same part of the studied region.

The Pliocene deposits of the marl series occupymost of the hilly region farther south of the region.Within this formation, sandstone, consolidated gravel,GEOLOGICAL STRUCTURE AND

HYDROGEOLOGICAL SETTING conglomerates, and marly limestones of lacustrine ori-

gin exist in locally restricted intercalations.The groundwater resources in the area studiedThe plain, north of the national highway, is

formed of Recent unconsolidated material consisting are located within the coastal aquifer system, whichconsist of the described Recent deposits. A fault zoneof sands, pebbles, breccias, and fine clay to silty sand

deposits, characterized by high degree of heterogene- along the national highway delineates the southernedge of the aquifer system, which is bounded by theity. Lateral continuity of the previously described

deposits is disrupted by Recent and older fluviotorren- Gulf of Korinthos to the north. To the east, the system


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Multivariate Statistical Analysis in Aquifer Hydrochemistry 137

is bounded by the Thyrrenian deposits extension to the are observed moving from the contemporary rivercourses to the areas in between. Anisotropy is extendedsea (approximately 2 km west of the city of Korinthos),

whereas to the west, it is bounded by the outcrop of to the vertical scale as well, where typically sand, clay,silt, and gravel interchanges and mixtures are observedthe marl series, by the city of Kiato (Fig. 1). The marl

series, which, as an entity, is considered an aquitard, in various thickness intercalations.The system consists of an unconfined phreaticslopes to the north and forms the bedrock of the

studied coastal alluvial aquifer system (Panagopoulos, aquifer superimposed on successive confined or semi-confined aquifers. Because of the heterogeneity, theseKoumantakis, and Stavropoulos, 1999).

The thickness of the plains deposits ranges from aquifers either are delimited by lateral lithological tran-sitions or extended into an adjacent unit. In parallel,30 m to 70 m, whereas along the fluviotorrential depos-

its of the Asopos River it exceeds 100 m. As a result within the secluded Thyrrenian conglomerate blocksoverhanging aquifers of low potential may develop.of their origin, the deposits are characterized by high

degree of heterogeneity and anisotropy. This also was As a result of the described geometry, a number ofdistinct aquifer units of local significance and smalldocumented from the study of lithological sections of

boreholes drilled in the region and the pumping test extent have developed and are evidenced by the obser-vation of varying piezometric heads in adjacent wellsanalyses conducted in some of them (Koumantakis

and others, 1999). Despite the exhibited heterogeneity, and boreholes. Despite the documented heterogenei-ties, however, it is suggested that on a regional scalehowever, it can be stated that on a regional scale there

is a progressive transgression from coarse material in a uniform aquifer may be considered on the basis thatobserved lithological anomalies are not extensive andthe south to fine material in the north. On the contrary,

abrupt lithological changes from coarse to fine material most groundwater level measurements are indicative

Figure 2. Groundwater sampling locations.


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of a single piezometric surface (Koumantakis and oth- with the calculated ionic balance error for each sample(as suggested by Appelo and Postma, 1993). The calcu-ers, 1999).

Recharge to the aquifer system originates from lated mean value (), maximum and minimum values,standard deviation (), and coefficient of variation (/direct infiltration of precipitation and riverbed infiltra-

tion. Lateral inflow from the fluviotorrential deposits, ), were calculated for each chemical variable, andare quoted in Table 1. The coefficient of variationespecially those of the Asopos River, and also from

the Tyrrhenian deposits across the southern edge of is considered an index of the relative dispersion ofeach variable.the basin is essential to the systems replenishment. It

has been demonstrated that returns from flood irriga- As earlier noted, available data consists of a rela-tively large number of analyzed samples (131) andtion, which traditionally is practiced during springtime

in the region, play a major role to aquifer recharge. considered parameters (15). It therefore was decidedthat these parameters should be analyzed within theGroundwater overexploitation, being practiced by

abstractions from about 1000 wells and boreholes, has framework of a multivariate system that divides theminto groups and reveals possible relationships betweenresulted in significant head decline that exceeded 2.0

m during hydrological year 19971998 (Koumantakis them. In this way, the large data set is reduced to asmall number of factors that are easier to evaluate inand others, 1999). Consequently, a large number of

wells located upstream in the south zone of the system order to deduce the main hydrochemical processes.Resultant factors consist of highly intercorrelated vari-have been temporarily or permanently incapacitated,

negative water balance is established, groundwater ables, where each factor represents a specific hydro-geochemical process that actually has formed thedeterioration has emerged, and extensive saline water

intrusion has started (Panagopoulos, Koumantakis, and variables fluctuation percentage. Thus, application ofmultivariate statistical analysis reduces the complexityStavropoulos, 1999). Indicative of the extent of

groundwater deterioration, is that in most of the areas, of the problem without any significant loss of informa-tion. This study utilized regression, factor, and trend-it is not suitable for human consumption, and in con-

siderable areas it is marginally acceptable even for surface analysis.irrigation use (Koumantakis and others, 1999; Panago-poulos, Koumantakis, and Stavropoulos, 1999).

RESULTS OF STATISTICAL ANALYSES

The calculated coefficients of variation (Table 1),AVAILABLE DATA AND APPLIEDMETHODS indicate wide distribution of NH4

+, K+, Na+, Cl, and

NO2

and limited distribution of SO42

, NO3

, andPO43. A study of the same table leads to the follow-As part of the groundwater hydrochemical study

carried out, an extensive sampling program was per- ing conclusions.The mean pH value denotes alkaline groundwaterformed in May 1997. A dense network consisting of

131 points spread over the studied region was designed character. Electrical conductivity values (E.C.) rangebetween 550 S/cm and 4,120 S/cm, the highest(Fig. 2), in order to obtain representative samples of the

entire aquifer system. The equipment of the National being related to seawater intrusion. Likewise, the meantotal dissolved solids (T.D.S.) value is as high as 954Technical University of Athens, Section of Geological

Sciences, facilitated laboratory determinations. In situ mg/L, and exhibits a relatively large standard devia-tion, thus indicating considerable fluctuations aroundmeasurements included electrical conductivity (E.C.),

pH, and water temperature. the mean value. Based on calculated total hardnessvalues (T.H.), most groundwater samples are character-Determinations of ion concentrations were carried

out using the following methods: colorimetric titration ized as very hard (T.H. 300 mg/L of equivalent

CaCO3).for Cl

and HCO3

, atomic absorption for Ca2

andMg2, flame photometry for K+ and Na+ and spectrum Mean nitrate concentration also is high at 73 mg/

L and is attributed to intensified agriculture activitiesphotometry for SO42, NO3

NO2, NH4

+, and PO43

(following the procedures described by Greenberg and linked with overfertilization during the last decades.About 75% of the examined samples exceeded theothers, 1985).

Based on the ion determinations, total hardness, maximum admissible concentration of 50mg/L set bythe European Union (E.U. Council, 1998), for wateralkalinity, and total dissolved solids were calculated.

Quality of the performed determinations was checked intended for human consumption.


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Table I. Statistical Characteristics of Considered Parameters, as Deduced by Results of Performed Hydrochemical Analyses of131 Samplesa

ValueStand. deviation Coefficient of variation

Parameter Mean () Min Max () (/)

PH 7.16 6.68 8.05 0.31 0.04

E.C. (S/cm) 1328 550 4120 568 0.42T.H. 669 94 1782 226 0.33

T.D.S. 954 340 3041 398 0.42

Ca2 165 31 285 43 0.26Mg2 65 16 290 42 0.64

Na+ 88 19 440 85 0.96K+ 9.5 1.0 130 20 2.1

HCO3 483 268 929 89 0.18

Cl 97 15 694 119 1.22SO4

2 216 0.1 860 141 0.65

NO3 73 0.3 285 49 0.67

NO2 0.04 0.0001 0.45 0.063 1.57

NH4+ 0.32 0.0 8.0 0.96 3.0

PO43 0.152 0.05 0.70 0.09 0.59

a Where applicable, values are in mg/L, unless otherwise stated.

Correlation Analysis (r0.22 and, 0.19, respectively), whereas SO42

have a correlation coefficient of r 0.55 with Cl

and r 0.46 with NO3.A widely used correlation criterion between two

Ions of opposite charge and equal valence num-variables is the simple correlation coefficient, whichberStrong correlations are indicated between Na+indicates the sufficiency of one variable to predictand Cl (r 0.84), Mg2 and SO4

2 (r 0.80), Ca2,the other (Davis, 1986). This coefficient is used toand SO4

2 (r 0.60), HCO3 and Na+ (r 0.70),determine correlation between the variables when the

with weaker correlations between K+ and Cl (r dependent (x) is only influenced by the independent0.50) and K+ and NO3

(r 0.30).(y) and vice versa. Despite the fact that this does Ions of the same type of charge and equal valencenot apply to hydrochemical data, simple correlation

numberCorrelation between Ca2 and Mg2 is notcoefficient is being used as a measure to describe asignificant (r 0.26) and likewise between NO3

,relationship between two variables.HCO3

,andNO2. Moreover, correlation between ClThree different correlation type generally are

and NO3 is relatively weak (r 0.54), as it is betweenidentified in aqueous systems (Douglas and Leo, 1977;

Na+ and K+ (r 0.47).Rao, Reddy, and Nayudu, 1996), namely: (i) a stronglyAs deduced from the correlation coefficientcompetitive correlation between ions of the same

matrix (Table 2), the value of electric conductivitycharge, but different valence number; (ii) a strong(E.C.) mainly depends upon the major ion concentra-chemical association between ions of opposite charge,tions (Ca2, Mg2, Na+, K+, HCO3

+, Cl, SO42,but equal valence number; and (iii) a noncompetitive

NO3). Under the assumption that E.C. is solely acorrelation between ions of the same type of charge

function of the major ion concentrations, the multipleand equal valence number. The correlation coefficient

regression model was applied between E.C. and thematrix between the examined ions, which was calcu- ion concentrations expressed (in mg/L), as follows inlated using linear regression analysis, is illustrated inEquation (1):Table 2. Study of this matrix concluded the following.

Ions of the same type of charge and differentE.C. b0 b1 Ca b2 Mg b3 Na b4 Kvalence numberA relatively strong but not signifi-

cant correlation between Mg2 and Na+ is observed b5 HCO3 b6 Cl b7 SO4 b8 NO3 (1)(r 0.58). A negative and statistically insignificantcorrelation is exhibited between Ca2 and Na+, K+ where E.C. is the electric conductivity of the sample


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Table 2. Correlation Coefficient Matrix of Considered Parameters

Ca2 Mg2 Na+ K+ HCO3 Cl SO4

2 NO3 NO2

NH4+ PO4

3 E.C. T.D.S. pH T.H.

Ca2 1.00

Mg2 0.26 1.00Na 0.22 0.58 1.00

K

0.19 0.26 0.47 1.00HCO3

0.17 0.34 0.70 0.10 1.00

Cl 0.02 0.80 0.84 0.50 0.40 1.00SO4

2 0.60 0.80 0.49 0.17 0.35 0.55 1.00NO3

0.42 0.58 0.26 0.30 0.03 0.54 0.46 1.00

NO2

0.28 0.05 0.02 0.02 0.03 0.04 0.15 0.14 1.00NH4

+0.37 0.07 0.29 0.02 0.35 0.09 0.17 0.14 0.17 1.00

PO43

0.39 0.11 0.59 0.63 0.37 0.47 0.03 0.14 0.06 0.35 1.00E.C. 0.65 0.86 0.88 0.56 0.64 0.93 0.76 0.58 0.09 0.06 0.19 1.00

T.D.S. 0.29 0.91 0.78 0.56 0.40 0.87 0.83 0.61 0.07 0.00 0.28 0.97 1.00pH 0.43 0.33 0.55 0.36 0.15 0.58 0.02 0.18 0.06 0.19 0.42 0.43 0.35 1.00

T.H. 0.64 0.90 0.34 0.10 0.21 0.60 0.90 0.61 0.12 0.22 0.09 0.74 0.84 0.06 1.00

(S/cm at 25C) and b0, . . . , b8 are the equations region: its major ion concentrations may be determinedbased on the percentages as mentioned. It has to beparameters. Application of the model described in

Equation (1), yielded: stressed, however, that acceptable results can be ex-pected only if the method is applied to E.C. measure-

E.C. 141 0.61 Ca 1.07 Mgments conducted in the same season as the samplingprogram on which the produced model is based, in 1.35 Na 1.52 K 0.85 HCO3order to ensure as high compatibility as possible to

2.14 Cl 0.87 SO4 1.25 NO3 (2) the prevailing hydrochemical evolution mechanismswithin the studied aquifer system at that time.The calculated multiple correlation coefficient is

as high as 0.99, standard error is 72.4, and R-squareis 0.98, thus indicating the model accounts for 98%of the observed distribution according to criteria set Factor Analysisby numerous researchers (Chatterjee and Price, 1991;

Bora-Senta and Moisiades, 1997). The F ratio is con- This type of analysis was applied in order tosiderably larger (658) than the criticalF12, 118,0.005 value reduce complexity of the initial data matrix by produc-of 2.54, which is retrieved from reference matrices.

ing a small number of factors that explain a largeIn the compiled model, every major ion contributes

amount of the total variance of the raw data. Hence,significantly, which, coupled with the results, leads to

factor analysis leads to the establishment of subgroupsthe conclusion that it is capable of forecasting the

of closely interrelated elements aiming at interpretingdependant variable (E.C.) values.

hydrochemical evolution mechanisms.Substituting the calculated major ion concentra-

The R-mode factor analysis has been applied ontions of a representative sample into Equation (2), the

hydrochemical data by a number of researchers andpercentage by which each ion contributes to the E.C.

comprises the following steps (Saager and Esselaar,value was determined to be:

1969; Briz-Kishore and Murali, 1989; Ruiz, Gomis,and Blasco, 1990; Voudouris, 1995; Voudouris andCations: Ca2 12.1%,

others, 1997): Data standardization, so that mean valueMg2 4.8%, Na+ 9.6%, K+ 1.4%is zero and standard deviation is one for every variable(Davis, 1986). This step is essential, as computationAnions: HCO3

36.7%, Cl 12.3%,of the correlation coefficient matrix requires normal

SO42 14.9%, NO3

8.2%distribution of all variables (Jayakumar and Siraz,1997). Next, the correlation coefficient matrix isIt, therefore, follows that given the electrical con-

ductivity value of a groundwater sample from the solved, the eigenvalues are calculated, and the factors


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Table 3. Loadings of Varimax rotated Four-Factor Model and that are correlated with eigenvalues greater than 1 areCommunality Values selected (Kaiser, 1958). The factors axes then are

rotated to new positions in order to achieve more accu-Commu-

rate interpretation of the results, on the basis of theVariables Factor I Factor II Factor III Factor IV nalitiesvarimax criterion (Voudouris and others, 1997). The

Ca2 0.56 0.52 0.41 0.03 0.76 factors loadings, which consist of their elements, form

Mg2

0.91 0.19 0.08 0.06 0.88 practically an assessment of the similarity degreeNa+ 0.62 0.46 0.56 0.06 0.92between the variables and the factors. Applicability ofK+ 0.19 0.80 0.14 0.08 0.71the method is assured by the communality values ofHCO3 0.34 0.08 0.83 0.14 0.83

Cl 0.70 0.59 0.21 0.02 0.89 the variables, which in fact are a sufficiency measure-SO4

2 0.92 0.11 0.00 0.07 0.87 ment of how a group of factors describes the fluctuationNO3

0.49 0.52 0.39 0.13 0.68 of a variable. In an accredited application of factorNO2

0.09 0.04 0.03 0.96 0.94analysis, the value of communalities for each variableNH4 0.18 0.48 0.50 0.47 0.73should be close to 1 (Ruiz, Gomis, and Blasco, 1990).PO43 0.00 0.79 0.30 0.10 0.72

T.H. 0.97 0.09 0.10 0.06 0.96 Finally, the factor scores were calculated, that is, thePH 0.16 0.63 0.36 0.32 0.65 influence each factor has on the sampling locations.T.D.S. 0.92 0.30 0.19 0.01 0.99 These values relate to the intensity of the chemicalE.C. 0.85 0.42 0.26 0.04 0.97

processes each factor represents. Extreme negative val-Variance (%) 45.1 21.1 9.0 6.7ues (1) reflect regions not affected by the specificprocesses each factor represents, in contrast with posi-tive values (1) that indicate areas under the influ-

Figure 3. Distribution of factor I scores.


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Figure 4. Distribution of factor II scores.

ence of the processes (Jayakumar and Siraz, 1997; The first factor accounts for 45.1% of the totalvariance of the database. It is addressed as the salinityBriz-Kishore and Murali, 1989).

The R-type factor analysis was applied here in factor, because of exhibited high loadings on T.H.(0.97), SO42 (0.92), T.D.S. (0.92), E.C. (0.85), Clorder to reduce the large hydrochemical database (131

samples), organize data into groups of similar charac- (0.70), and Na+ (0.62). This factor is correlated toseawater intrusion of the coastal aquifer system, whichteristics, identify the weight of each parameter, and

detect correlations between them. Application of the results to the observed high values of the consideredparameters. As deduced from the geographical distri-R-type factor analysis concluded with the selection of

four factors, which explained 82% of the total variance. bution of the factors values (Fig. 3), the coastal areabetween Lecheo and Assos is characterized by highFor each factor, its influence to the sampling location

was determined by calculating its score. The eigenval- values, in contrast with the area around the communesof Poulitsa, Velo, and Vochaiko, where the values areues of the four selected factors are 6.76, 3.16, 1.35,

and 1.01, respectively. The concluded factor loadings negative. It is therefore indicated, that the formerregion is influenced strongly by seawater intrusion.after rotation are given in Table 3, whereas in Figures

36 the geographical distribution of the factors based Factor II accounts for 21.1% of the total database

variance. It shows high loadings on K+

(0.80) andon their values is illustrated. Study of these maps mayindicate the importance of each hydrochemical process PO4

3 (0.79) and relatively high loadings on NO3

(0.52) (Fig. 4). Potassium concentration in seawateras represented by the selected factors, with referenceto their geographical distribution. Values of commu- is higher, however, because K+ as a parameter is not

included in the first factor and its correlation to Na+nalities range between 0.65 and 0.99, thus indicatingthat selected factors adequately represent the entire and Cl is relatively low (r 0.47 and r 0.50,

respectively) as quoted in Table 2, it is concluded thatrange of variables fluctuations.


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Figure 5. Distribution of factor III scores.

this second factor is related to mixed-type fertilizers ammonium nitrogen oxidation. They are formed ofbacteria-induced oxidation of organic nitrogenthat contain nitrogen, phosphate, and potassium

(NPK). Study of the geographical distribution of fac- released from decaying fauna and flora, as well asfrom microbial-induced partial denitrification in envi-tors values suggests that high values are concentratedin the Ancient Korinthos region, as well as in the ronments characterized by oxygen depletion. High val-

ues of this factor are depicted in the areas of Katosouthern part of Lecheo commune and the greater Vra-chati area, where extensive application of fertilizers to Assos, Velo, Evagelistria, and Perigiali, whereas nega-

tive values are exhibited at the communes of Poulitsa,cultivation is known to be practiced.Factor III exhibits high loadings with respect to Kokkoni, and Krines (Fig. 6)

HCO3 (0.83) and accounts for 9% of the total variance

(Fig. 5) Bicarbonates, which prevail in freshwater, arerelated to soil carbon dioxide dissolution and dissocia- Trend-Surface Analysistion of the unstable form of carbonic acid. Stronglypositive values of this factor are exhibited in the areas Trend-surface analysis is a simple multiple

regression technique where independent variables areof Kato Assos and Vrachati and slightly positive values

in the area of Poulitsa. The observed negative values based upon X and Y coordinates. This method attemptsto generate a good fit surface expressed by an equation.of this factor at the communes of Krines and Moulki

are attributed to low carbon dioxide dissolution To do so, the dependent variableZ is described as afunction of its geographical location. The so-calledpotential.

Factor IV accounts for 6.7% of the total variance trend surface can be either planar or curved. Trend-surface analysis allows for two-part separation of sur-and exhibits high loadings with respect to NO2 (0.96).

Nitrites are a relatively unstable intermediate form of face distribution (Chen and others, 1997):


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Figure 6. Distribution of factor IV scores.

Ho Ht Hr E where Z, chloride ion concentrations (mg/L), X, Ygeographical coordinates, and a, b, c, d, e, f, g, h, i,

where Ho

observed value, Ht

trend value, Hr

j constants.residual value, and E random error.To determine the credibility of the aforemen-

The random error value can be low and thus omit-tioned equation, theR2 coefficient was calculated using

ted. According to Watney (1985), trend values repre-the following equation (Papatheodorou and Lambrakis,

sent regional trends of the parameters distribution,1997), and was 0.73:

whereas residuals represent local anomalies. Hence,conclusions can be drawn regarding the reasons that R2 (VarZ VarZr)/VarZhave formed the surface distributions, although in par-allel, similarities and differences between various sur- whereVarZ variance of the initial data Zvalues and

VarZr residuals variance.face distributions can be examined on the basis ofcredible statistical criteria. Study of the chloride concentration trend map, as

illustrated in Figure 7, indicates the following. HighIn this paper, a third-degree (cubic) trend surfacewas computed in order to study the areal distribution chloride concentrations are depicted in the coastal

region between Vrachati and Ancient Korinthos. Moreof chloride ion concentrations through which seawaterintrusion zones in the study area were delineated. The specifically, the highest values (above 300 mg/L) are

observed in the areas of Lecheo and Ancient Korinthoscubic trend surface is represented by a saddle-shapedsurface, described by the following equation: communes. This region coincides to the geographical

distribution of factor I, which, as earlier described inZ a bX cY dX2 eXY f Y2 gX3

the performed factor analysis, is indicative of seawaterintrusion. A gradual decline of chloride concentrations hX2Y iXY2 jY3


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Figure 7. Chloride concentration equal trend curves.

from this saline zone to the southwestern part of the tion of multiple regression analysis suggested that elec-

tric conductivity may be described properly as astudied region (Krines Commune) is observed. Thelatter area is not influenced by seawater intrusion and function of major ion concentrations, hence, by mea-suring it in the field, major ion concentrations can bechloride concentrations there are lower than 40 mg/L.determined. It has to be stressed, however, that validcalculations rely upon electric conductivity measure-ments conducted over the same period as the samplingCONCLUSIONSon which the equation derivation was based. Shouldhydrochemical evolution of the system become altered,Groundwater in the studied region seems to be

hard to very hard, as total hardness expressed in CaCO3 it is believed that the derived equation will not be valid.Application of the R-type factor analysis, resultedusually exceeds 300 mg/L Nitrate concentrations are

high over the region, having an average value of 73 in four factors which adequately describe 82% of thetotal data variance and are having the following load-mg/L. These concentrations are attributed to the

agricultural practices of the region, under which inten- ings: (I) T.H., SO42, T.D.S., Mg2, E.C., Cl, and

Na+

; (II) K+

and PO43

; (III) HCO3

; and (IV) NO2

.sive cultivation is carried out, supported by excessiveapplication of NPK type fertilizers, where flood irriga- Factor I is related to seawater intrusion, factor II to

application of fertilizers, factor III to groundwater car-tion is applied.A significant correlation between Na+ and Cl, bon dioxide dissolution potential, and factor IV to the

aquifer oxidation potential and the existence of organicMg2 and SO42, HCO3

and Na+ is depicted, whereascorrelation between Cl and NO3

, Ca2 and SO42, matter within the aquifer matrix. Finally, application

of trend-surface analysis revealed a seawater intrusionK+ and Cl, Mg2,andNa+ seems to be lower. Applica-


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Chen, H., Zhang, Y., Wang, X., Ren, Z., and Li, L., 1997, Salt waterzone extending between Vrachati and Ancientintrusion in the lower reaches of the Weihe River, Shandong

Korinthos. province, China: Hydrogeology Jour., v. 5, no. 3, p. 8288.Daskalaki, P., and Voudouris, K., 1997, Hydrochemistry of runoffApplication of multivariate statistical analysis on

waters in Epirus, NW Greece: R-mode factor analysis of data,131 samples collected from the alluvial aquifer systemin Proc. Intern. Symp. Eng. Geology and the Environment:

of the northern coastal zone of Korinthos Prefecture, A. A. Balkema, Rotterdam, p. 17511756.Davis, J. C., 1986, Statistics and data analysis in geology (2ndsouthern Greece, helped to delineate the major hydro-

edn.): John Wiley & Sons, New York, 646 p.chemical processes that control hydrochemical evolu- Douglas, E. B., and Leo, W. N., 1977, Hydrochemical relationshipstion of the region. It was concluded that such statistical using partial correlation coefficients: Water Resources Bull.,

v. 13, no. 4, p. 843846.methods may augment conventional hydrochemicalE.U. Council, 1998, Council Directive 98/83 about water qualityanalyses, by defining processes and trends indicated

intended for human consumption, in Official Paper of theby the latter, but not clearly proved for a number of European Communities: EC, Brussels, v. L330, p. 3254.

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Multivariate statistics in groundwater