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Environmental Earth Sciences ISSN 1866-6280Volume 65Number 3 Environ Earth Sci (2012) 65:823-833DOI 10.1007/s12665-011-1127-4

Factors affecting the distribution ofpotentially toxic elements in surfacesoils around an industrialized area ofnorthwestern Greece

Alexandra Petrotou, KonstantinosSkordas, Georgios Papastergios &Anestis Filippidis

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ORIGINAL ARTICLE

Factors affecting the distribution of potentially toxic elementsin surface soils around an industrialized area of northwesternGreece

Alexandra Petrotou • Konstantinos Skordas •

Georgios Papastergios • Anestis Filippidis

Received: 1 April 2010 / Accepted: 13 May 2011 / Published online: 26 May 2011

� Springer-Verlag 2011

Abstract In order to investigate the factors influencing

the distribution of 32 potentially toxic elements in the

Ptolemais–Kozani basin, northwestern Greece, 38 soil

samples were collected and analyzed. Concentrations of

Al, Ca, Fe, K, Mg, Mn, Na, P, Ti, Ba, Co, Cr, Cu, La, Li,

Ni, Pb, Sc, Sr, V, Y, and Zn were determined by ICP-AES

and concentrations of As, Bi, Cd, Cs, Mo, Rb, Sb, Th, Tl,

and U by ICP-MS. Bivariate analysis, principal component

analysis, and geostatistical analysis were employed to

investigate the factors influencing the distribution of the

elements determined in the study area. The results indicate

that the distribution of the majority of elements deter-

mined, especially for Cr, Ni, and associated elements, is

greatly influenced by the geology and geomorphology of

the study area. Principal component analysis has yielded

four factors that explain over 77% of the total variance in

the data. These factors are as follows: lithophilic elements

that are associated with Al silicates minerals of K (factor I:

29.4%), ultramafic rocks (factor II: 20.5%), elements that

are coprecipitated with Fe and Mn oxides (factor III:

18.0%), and anthropogenic activities (factor IV: 9.3%).

The anthropogenic activities that influence the distribution

of several potentially toxic elements (i.e., Cd, Cu, Pb, Zn)

are agricultural practices and the deposition of fly ash in the

vicinity of the local power stations.

Keywords Soil � Geochemistry � Environment �Principal component analysis � GIS � Ptolemais

Introduction

Natural processes (e.g., weathering, pedogenesis) and

human activities (e.g., industry, agriculture) are the main

factors influencing the content of potentially toxic elements

(PTEs) in soils. The study of PTEs’ distribution in soils is

an important and, at the same time, complex task, because

soils are considered as the principal sinks for such elements

and, moreover, appropriate means for monitoring contam-

ination (Cui et al. 2005; Fernandez-Turiel et al. 2001;

Hesterberg 1998; Kabata-Pendias and Pendias 2001;

Kelepertsis et al. 2006; Papastergios et al. 2010a, 2011;

Skordas and Kelepertsis 2005; Stalikas et al. 1997). High

levels of PTEs in soils may result in an increased uptake of

PTEs by crops and vegetables, which in its turn may have a

negative effect on animals and, consequently human health

Electronic supplementary material The online version of thisarticle (doi:10.1007/s12665-011-1127-4) contains supplementarymaterial, which is available to authorized users.

A. Petrotou

Department of Earth Sciences, Royal Holloway

University of London, Egham Hill, Egham TW20 0EX, UK

Present Address:A. Petrotou

Frixou 2, 41222 Larissa, Greece

e-mail: thsalexa@hotmail.com

K. Skordas

Department of Ichthyology and Aquatic Environment,

University of Thessaly, Fitokou str., N. Ionia,

38446 Volos, Greece

e-mail: kskord@uth.gr

G. Papastergios (&) � A. Filippidis

Department of Mineralogy-Petrology-Economic Geology,

School of Geology, Aristotle University of Thessaloniki,

54124 Thessaloniki, Greece

e-mail: gpapaste@geo.auth.gr; gpapastergios@gmail.com

A. Filippidis

e-mail: anestis@geo.auth.gr

123

Environ Earth Sci (2012) 65:823–833

DOI 10.1007/s12665-011-1127-4

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(Kabata-Pendias and Pendias 2001; Papadopoulos et al.

2007).

Approximately more than 60% of the electrical power

requirements of Greece are generated through the utiliza-

tion of lignite (Kavouridis 2008; Kavouridis and Koukou-

zas 2008; Kaldellis et al. 2009). The lignite used in these

power plants is characterized by high moisture levels, high

calcium and ash content, and low calorific value and are

thus considered as one of the poorest fossil fuels world-

wide. The quality of the mined lignite is strongly connected

to the quality of the organic matter (rank), the inorganic

impurities, and the nature of the sterile layers that are

excavated with the lignite (Christanis et al. 1998; Fernan-

dez-Turiel et al. 2004; Filippidis et al. 1996; Georgako-

poulos et al. 1995; Kolovos et al. 2002a, b, c; Sotiropoulos

et al. 2005). Using such fuels may produce huge amounts

of flue gases and other types of wastes (i.e., bottom ash, fly

ash, water polluted with radionuclides and PTEs, etc.) that

are disposed off in the environment (Fernandez-Turiel et al.

2004; Karamanis et al. 2009; Kolovos et al. 2002a; Pa-

pastergios et al. 2005, 2007; Sotiropoulos et al. 2005;

Triantafyllou 2003; Vatalis and Kaliampakos 2006).

Additionally, slagging and fouling severely affect the

power plants’ use of these lignites (Fernandez-Turiel et al.

2004).

The Western Macedonian Lignite Center (WMLC) in

the Florina–Ptolemais–Kozani basin, northern Greece, and

the Megalopolis Lignite Center (MLC) in the Peloponnese,

southern Greece, are the two main coal-mining districts in

the country; most of the lignite used in the aforementioned

power plants is mined from these centers. The lignite

mined in these opencast mines is used to generate elec-

tricity in the power plants of Liptol, Amynteon, Ptolemais,

Kardia and Agios Dimitrios in the WMLC, and Megalop-

olis A and Megalopolis B in the MLC. These plants con-

sume more than 70 Mt of lignite annually and produce

almost 13 Mt of fly ash per year, thus making Greece the

second lignite producer in the EU and fourth in the world

(Fernandez-Turiel et al. 2004; Kaldellis et al. 2009; Ka-

vouridis 2008; Kavouridis and Koukouzas 2008; Kolovos

et al. 2002a, b; Triantafyllou et al. 2006). Fly ash particles

are regarded as highly contaminating, since on their high

surface area may accumulate PTEs and other pollutants

(Fernandez-Turiel et al. 2004; Iordanidis et al. 2008a, b;

Kolovos et al. 2002a, b; Papastergios et al. 2005, 2007;

Querol et al. 1996; Tsikritzis 2005).

Knowledge of the content of PTEs in soils, as well as the

origin of these contaminants constitute priority objectives

in the European Union, in this case, especially for countries

that utilize fossil fuels (i.e., lignite) as an energy source.

The results and the methodology followed in the present

work offer a new insight regarding the distribution and

origin of several PTEs in the surface soils of an

industrialized area that utilizes lignite for the production of

electricity and, since, numerous similar areas exist around

the world, could prove useful in other occasions, as well.

Study area

The study area is situated in the Ptolemais–Kozani basin,

northwestern Greece and includes the WMLC and two

power plants, namely these of Kardia, and Agios Dimitrios

(Fig. 1a, b). The cultivation of mainly corn, wheat, alfalfa,

potatoes, tobacco, various vegetables, and fruit trees in

some small parts of the study area is common (Gikas et al.

2009). In the area, weak to moderate winds blow, mostly

from the north and northwest (Gikas et al. 2009; Papado-

poulos et al. 2007; Samara 2005).

Geological setting

The basement of the basin consists mainly of Palaeozoic

and Mesozoic metamorphic and plutonic rocks underlying

Cretaceous limestone and flysch (Fig. 1b). The basement

rocks are divided into four distinctive tectonic units (Di-

amantopoulos 2005, 2006; Mountrakis 1983; van Hins-

bergen et al. 2006):

1. The pre-Alpine Pelagonian Basement, separated into

the Schist sub-unit, the Gneiss sub-unit, the granitoid

mylonites and the granites;

2. The Almopia Unit, with Triassic-Lower Jurassic

marbles and meta-sediments of Late Jurassic age;

3. The Ultramafic–mafic Unit of Mesozoic age, with

ophiolites, composed by many occurrences of serpen-

tines; and

4. The Cretaceous Transgression Unit, with rudist-bear-

ing, platform limestones and Maastrichtian flysch.

The Neogene-Quaternary sediments of the basin are

characterized as limnic marls and clays and host the lignite

seams (Kolovos et al. 2002b; Koufos and Pavlides 1988;

Owen et al. 2010; Pavlides 1985; Pavlides and Mountrakis

1987).

Materials and methods

Thirty-eight surface (0–15 cm) soil samples were col-

lected, in distances greater than 50 m from the road, in

order to avoid contamination associated with vehicle

emissions. From each sampling site around 1 kg soil

(obtained from 5 different locations) was taken, in order to

collect representative samples. The five sub-samples were

used to composite one sample, which was eventually

analyzed. Sub-samples improve sampling precision and

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Fig. 1 a Digital elevation

model (DEM) of the study area

and sample locations.

b Simplified geological map of

the study area and sample

locations

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thus, minimize random error derived from the heteroge-

neity of the site. All samples were dried at 60�C, sieved

under 90 lm, and stored in high-density polyethylene bags,

to prevent contamination. The fine fraction of the soil was

selected for analysis because it is probable that larger

particles and rock fragments would contain, on a weight

basis, considerably less contaminant than the smaller ones.

Approximately 0.2 g of the powder samples were

weighed and placed into 25 ml polytetrafluoroethylene

crucibles. Then, 6 ml of HClO4/HF acid (1:2) was added to

each crucible. The tray of crucibles was placed on a hot-

plate in the fume cupboard and evaporated to dryness for

3–4 h. After cooling, 2 ml of HNO3 were added to each

crucible. Each crucible was toped up with distilled water to

3/4 and warmed on the hotplate for 15–20 min. Once the

crucibles were cooled, solutions of 20.8 g on a top pan

balance were made by using distilled water. These solu-

tions were placed in appropriately labeled tubes and, after

shaking well, they were ready for analysis.

The concentrations of Al, Ca, Fe, K, Mg, Mn, Na, P, Ti,

Ba, Co, Cr, Cu, La, Li, Ni, Pb, Sc, Sr, V, Y, and Zn were

determined by Inductively Coupled Plasma–Atomic

Emission Spectrometry (ICP–AES), while the concentra-

tions of As, Bi, Cd, Cs, Mo, Rb, Sb, Th, Tl, and U were

determined by Inductively Coupled Plasma–Mass Spec-

trometry (ICP–MS). The analyses were performed at the

Royal Holloway University of London. For reasons of

quality control, several blanks, two certified reference

materials (GBW07312 and GBW07405), and four internal

standards of the latter University were used during the

procedure. The recoveries of the reference materials ran-

ged, for the majority of the determined elements, between

90 and 105%. Some exceptions were noted (i.e., Sc: 30%),

probably due to the low concentrations of these elements in

the reference materials. Furthermore, the instrumental and

sampling precisions were estimated. Both factors were

satisfactory for the work objectives (average values of RSD

were 1.85 and 3.23%, respectively).

Results and discussion

Summary statistics for the elements determined in the

present study are presented in Table 1. The most abundant

Table 1 Descriptive statistics for the elements determined in the present study

Element (g kg-1) Al Ca Fe K Mg Mn Na P Ti

Average 60.6 81.1 52.3 13.3 23.8 1.0 4.7 0.8 3.2

Median 62.2 71.0 54.1 13.4 17.3 1.0 4.4 0.7 3.4

Minimum 34.7 10.0 26.9 5.8 9.4 0.4 0.8 0.3 1.4

Maximum 84.2 205.4 79.0 22.2 93.4 1.7 10.6 2.6 4.6

Std deviation 13.9 53.2 12.2 3.4 17.2 0.3 2.0 0.4 0.8

Element (mg kg-1) As Ba Bi Cd Co Cr Cs Cu

Average 15.1 301.7 0.7 0.5 21.9 345.8 5.2 36.9

Median 12.7 301.2 0.7 0.5 22.0 303.8 5.0 32.9

Minimum 6.5 183.9 0.6 0.2 10.2 127.0 3.0 19.2

Maximum 44.2 467.7 1.0 0.9 59.8 1501.8 11.3 226.8

Std deviation 8.4 75.4 0.1 0.2 8.6 221.9 1.6 32.3

Element (mg kg-1) La Li Mo Ni Pb Rb Sb Sc

Average 31.5 40.1 0.7 286.8 17.2 82.1 0.7 13.3

Median 33.0 38.8 0.7 246.6 15.1 81.5 0.6 13.5

Minimum 14.1 12.5 0.4 85.4 4.3 46.6 0.1 7.5

Maximum 48.9 70.9 1.0 1075.3 79.0 126.2 2.1 19.9

Std deviation 9.0 11.9 0.2 173.8 12.6 20.2 0.4 3.0

Element (mg kg-1) Sr Th Tl U V Y Zn

Average 104.7 11.0 0.5 2.0 87.5 23.5 80.0

Median 99.7 10.6 0.5 2.1 89.9 23.9 80.3

Minimum 42.8 6.2 0.3 1.3 48.7 12.4 52.9

Maximum 233.0 24.4 1.0 3.6 120.9 35.9 112.9

Std deviation 35.3 3.6 0.2 0.5 19.3 6.7 16.0

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element is Ca with an average concentration of

81.1 g kg-1, followed by Al, Fe, Mg, K, Na, Ti, Mn, and P,

in descending order. From the trace elements, Cr, Ba, Ni,

and Sr have average concentrations above 100 mg kg-1, V,

Rb, Zn, Zr, Li, Cu, La, Y, Co, Pb, As, Sc, Th, Cs, and U

have average concentrations between 100 and 1 mg kg-1

and Bi, Mo, Sb, Cd, and Tl have average concentrations

below 1 mg kg-1.

Bivariate analysis

For most of the elements, the arithmetic mean (average)

was almost equal to the median (Table 1); thus, it was

assumed that the elements determined approximate the

normal distribution. The correlation matrix regarding all

the elements was computed and, for reasons of conciseness,

is presented as supplementary material (Appendix 1). SPSS

16.0 was applied for all the statistical analyses.

Examining Appendix 1 reveals that strong, positive

correlation exists among many elements (i.e., between Fe,

Mg, As, Co, Cr, and Ni, between Cu and Pb, between P and

Cd, between Ca and Sr, etc.). This observation suggests

common sources for the elements with high correlation

coefficients. For example, elements such as Cr and Ni may

have their primary source in the ultramafic rocks (ophio-

lites) that occur in the area, while the elevated concentra-

tions of Ca and Sr to the carbonate rocks in the margins of

the basin. Calcium correlates positively with Sr, and both

correlate negatively with almost all the other determined

elements. The latter observations are clearly depicted in the

interpolation maps created for the study area (Figs. 2, 3, 4,

5).

Principal component analysis

In order to further evaluate the geochemical data and the

associations among the elements determined, principal

component analysis (PCA) was applied. PCA is often used

in exploratory data analysis in order to (Kelepertsis et al.

2006; Skordas and Kelepertsis 2005; Skordas et al. 2007):

(a) reduce the complex pattern of correlations among many

variables to simpler sets of relationships between fewer

variables, (b) select a subset of variables from a larger set,

based on which original variables have the highest corre-

lations with the factors, and (c) uncover the underlying

structure of a relatively large set of variables. In the present

study, PCA is used to elucidate the data with the aim to

explain the dimensions associated with data variability, to

investigate variables (elements) and sources, and to deter-

mine which elements have a common origin. Due to the

restricted number of samples, the results of factor analysis

are merely indicative. However, an attempt was made to

explain the elements with common origin and identify their

sources.

The PCA yielded four factors, explaining over 77% of

the total variance in the data (Table 2). Factor I explains

29.4% of the total variance and is loaded by Al, K, Ba, Bi,

Cs, La, Li, Rb, Th, Tl, U, and V, with factor coefficients

Fig. 2 Interpolation maps of

the Al, Ca, Fe and K

concentrations

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oscillating between 0.54 and 0.97. This factor probably

indicates a group of mainly lithophilic elements that are

associated with Al–silicate minerals of K. Their most

possible origin is the weathering of rocks, such as granites

and gneisses, which occur in the broad area (Fig. 1b).

Factor II explains 20.5% of the total variance and is

loaded by Fe, Mg, As, Co, Cr, Mn, Ni, Sb, and Sc, with

factor coefficients oscillating between 0.56 and 0.94. The

strong associations among Ni, Cr, Co, and Mg in the soils

suggest that they have the same input sources and common

Fig. 3 Interpolation maps of

the Mg, Mn, Na and P

concentrations

Fig. 4 Interpolation maps of

the As, Co, Cr and Ni

concentrations

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geochemical characteristics reflecting the influence of the

ultramafic rocks (ophiolites) on the soils. In Table 3 are

given the average values of Cr and Ni reported for several

other Greek regions, as well as the target and intervention

values of the Dutch list regarding these elements. In gen-

eral, all other regions used for comparison have lower

values, with the exception of central Euboea. Details

regarding the comparison with international guidelines can

be found in Petrotou et al. (2010).

Factor III explains 18.0% of the total variance and is

loaded by Al, Fe, Mn, Na, La, Sc, Tl, V, Y, and Zn, with

factor coefficients oscillating between 0.51 and 0.86. This

factor possibly reflects the elements that are coprecipitated

with Fe and Mn oxides. Once Fe2? is released from silicate

minerals, it becomes oxidized and precipitates as Fe oxides

and hydrous oxides. Alkaline conditions promote the pre-

cipitation of Fe (Kabata-Pendias and Pendias 2001). Dur-

ing weathering, Mn compounds are oxidized under

atmospheric conditions and the released Mn oxides are

reprecipitated in the form of secondary Mn minerals.

According to Bartlett (1986), the Mn concretions in soils

are reported to accumulate Fe and several trace elements.

Usually, As is associated with iron oxides and hydrous

oxides; however, this is not the case in this study. A pos-

sible explanation for this behavior could be that As is found

in noteworthy amounts (4 mg kg-1, Georgakopoulos et al.

2002a) in the ophiolites of the area and, thus, is associated

with factor II and not factor III. This hypothesis was

reached because As is more closely correlated with ele-

ments deriving from the ultramafic rocks (i.e., Co, Cr, Ni)

than with Fe (see Appendix 1). In addition, the spatial

distribution of As is almost identical to Co, Cr, and Ni, thus

supporting the former speculation (Figs. 3, 4).

Factor IV explains 9.3% of the total variance and is

loaded by P, Cu, Cd, Pb, and Zn with factor coefficients

oscillating between 0.57 and 0.78. This factor possibly

reflects anthropogenic activities such as agriculture,

through the application of phosphate and other fertilizers

and pesticides, used in certain parts of the area, or/and the

local power plants. Other studies (Gikas et al. 2009; Pa-

padopoulos et al. 2007) have also reported the extensive

cultivation taking place in the area as a source of P and,

possibly, associated elements. Regularly, phosphoric fer-

tilizer products contain large amounts of Cu, Cd, Pb, Zn,

and other elements, which are considered as essential

nutrients to plants. That is why elevated values of these

elements have been reported in areas where these products

are being manufactured or applied (Kabata-Pendias and

Pendias 2001; Papastergios et al. 2009, 2010b, 2011).

Furthermore, a comparison between the chemistry of the

fly ash (as given by Georgakopoulos et al. 2002a, b, c;

Papastergios et al. 2007) and the surface soils of the study

area (Table 4) reveals that they have similar concentra-

tions regarding the elements of factor IV. According to

Fig. 5 Interpolation maps of

the Cd, Cu, Pb and Zn

concentrations

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Iordanidis et al. (2008a, b) and Petaloti et al. (2006), larger

fly ash particles (C10 lm) are mostly deposited in the

vicinity of power stations; hence, surface soils around these

locations could be enriched in elements (i.e., Cu, Cd, Pb

and Zn) found with elevated concentrations in fly ash.

Since the most common wind direction is from the north

and northwest (Gikas et al. 2009; Papadopoulos et al. 2007;

Samara 2005), the elevated concentrations that these ele-

ments have near the power stations could be attributed to

the deposition of fly ash. Finally, a minor contribution of

factor IV could be invoked from the weathering of flysch

sedimentary rocks.

Spatial analysis

Geochemical mapping is an important tool in the hands of

environmental scientists who make efforts to assess the

spatial associations between areas with elevated concen-

trations of PTEs and their potential sources, either natural

or anthropogenic (Bolviken et al. 1996; Chen et al. 1999,

2001; FOREGS 2005; Kelepertsis et al. 2006; Papastergios

et al. 2010a, 2011; Salminen 2007). In order to study the

distribution of the determined elements in the Ptolemais–

Kozani area, interpolation maps of their concentrations

Table 2 Varimax component loadings of four factors and percentage

of variance explained for 32 variables (elements)

Element Factor I Factor II Factor III Factor IV

Al 0.75 – 0.62 –

Ca – – – –

Fe – 0.71 0.51 –

K 0.79 – – –

Mg – 0.78 – –

Mn – 0.56 0.57 –

Na – – 0.68 –

P – – – 0.57

Ti – – 0.86 –

As – 0.74 – –

Ba 0.83 – – –

Bi 0.81 – – –

Cd – – – 0.78

Co – 0.94 – –

Cr – 0.94 – –

Cs 0.97 – – –

Cu – – – 0.74

La 0.73 – 0.57 –

Li 0.61 – – –

Mo – – – –

Ni – 0.93 – –

Pb – – – 0.66

Rb 0.88 – – –

Sb – 0.85 – –

Sc – 0.62 0.54 –

Sr – 0.63 – –

Th 0.96 – – –

Tl 0.95 – – –

U 0.58 – – –

V 0.54 – 0.73 –

Y – – 0.75 –

Zn – – 0.55 0.59

Variance (%) 29.4 20.5 18.0 9.3

Extraction method: Principal component analysis. Rotation method:

Varimax with Kaiser Normalization. Rotation converged in 8 itera-

tions. Loadings that are not included in the table are \0.5

Table 3 Average concentrations of Cr and Ni in surface soils of the

study area and other Greek regions

Average (mg kg-1) Cr Ni

Present study 345.8 286.8

Ptolemais–Kozani (1) 262.2 190.0

Ptolemais–Kozani (2) 77.5 161.3

Thessalia, central Greece (3) 299.0 189.0

Kavala, northern Greece (4) 11.1* 11.0*

Kavala, northern Greece (5) 16.1 14.9

Nestor River, northern Greece (6) 25.5 20.3

Central Euboea (7) 1300.0 2800.0

Dutch list target value (8) 100.0 35.0

Dutch list intervention value (8) 380.0 210.0

Target and intervention values of the Dutch list are also included: (1)

Georgakopoulos et al. (2002a), (2) Stalikas et al. (1997), (3) Skordas

et al. (2010), (4) Papastergios et al. (2011), (5) Papastergios et al.

(2010b), (6) Papastergios et al. (2009), (7) Megremi (2010), (8)

Petrotou et al. (2010), values marked with asterisk represent geo-

metric average

Table 4 Average concentrations of Cd, Cr Cu, Ni, Pb and Zn in

surface soils of the study area and fly ash produced in the power

stations of the study area

Average (mg kg-1) Cd Cr Cu Ni Pb Zn

Soils

Present study 0.5 345.8 36.9 286.8 17.2 80.0

Fly ash

Georgakopoulos et al.

(2002a)

1.1 339.9 60.9 332.4 30.6 249.1

Georgakopoulos et al.

(2002b)

1.0 154.5 52.0 209.7 24.3 97.7

Georgakopoulos et al.

(2002c)

1.5 148.9 74.9 217.4 27.2 130.0

Papastergios et al.

(2007)

1.3 296.2 39.0 386.5 30.7 91.3

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were created with the aid of ArcGIS software. Several

interpolation methods were tested (i.e., kriging, spline, etc.)

but the one that produced the most reliable output, for the

present study, was inverse distance weighted (IDW). This

deterministic method produced a smoother and easier-to-

interpret surface than the other interpolation tools applied.

In order to highlight the possible effect of the area’s geo-

morphology on the distribution of the determined elements,

a digital elevation model (DEM) of the study area was also

created (Fig. 1a). For the determination of the concentra-

tion classes the method of natural breaks, provided by the

ArcGIS software, was used. It is important to notice that

although the interpolation method calculates a surface for

the entire study area, the prediction inside the WMLC is

not valid, since after the extensive mining of that area, no

surface soils are present.

The elements that are highly correlated and belong to the

same factors yielded by the PCA have a very similar distri-

bution (i.e., Mg and Mn, Cr and Ni and so on). The distri-

bution of the elements seems to be strongly influenced by the

local geology and geomorphology. The study area is situated

inside a basin (Fig. 1a) and is bordering with large moun-

tains to the SW (Askion Mountain) and the NE (Vermio

Mountain). Hence, all erosion and leaching products even-

tually end up inside the Ptolemais–Kozani tectonic graven.

In particular, elevated Al concentrations are noted only

near limestones, flysch, and schists (Figs. 1, 2). On the

contrary, elevated Fe, Mg, Mn, As, Co, Cr Ni, and Sb

concentrations are strongly influenced by the presence of

ophiolites at the SW and SE parts of the study area

(Figs. 1–4). Although the Ptolemais–Kozani basin is sur-

rounded by limestones, high values of Ca are encountered

at the east, northeast, and northwest sides of the basin

(Fig. 2b). Phosphorus’ highest values are noted in the

center of the basin, to the south of the WMLC. Since P is a

major constituent of the fertilizers used in the area for

agricultural practices, quantities leached from these prod-

ucts could end up at that part of the basin due to mor-

phological reasons (the lowest elevations are noted in that

section of the study area).

The high values of Cd, Cu, Pb, and Zn located to the west-

northwest areas of the power plants’ facilities (Fig. 5) are,

probably, due to the deposition of large (C10 lm) fly ash

particles in the vicinity of the power plants. Furthermore, the

elevated concentrations that Cd and Zn demonstrate in the

center of the basin may be attributed to agricultural practices.

A common characteristic for almost all the elements is a

zone (area) with low concentrations situated to the south of

the WMLC and which has a SW–NE direction. Exception

to this feature is the elements associated with the ophiolites

(i.e., Cr, Ni) located at the SW and SE parts of the study

area. Once again, this feature is a consequence of the area’s

geology and morphology.

Conclusions

Bivariate analysis has revealed that strong positive rela-

tionships exist between several elements in the study area.

Principal Component Analysis has yielded four factors

affecting the distribution of the determined elements in the

Ptolemais–Kozani area. These factors are lithophilic ele-

ments that are associated with Al–silicate minerals of K

(factor I), ultrabasic rocks (factor II), elements that are

coprecipitated with Fe and Mn oxides (factor III), and

anthropogenic activities (factor IV). The four factors

account for 77% of the data variability. The digital eleva-

tion model and the interpolation maps created for the study

area indicate that the distribution of the elements deter-

mined is strongly influenced by its geology and geomor-

phology. However, the activities of the local power

stations, through the deposition of fly ash particles, and

agricultural practices have changed the geogenic contents

and distribution of some elements (i.e., Cd, Cu, Pb, Zn).

These observations are in agreement with the conclusions

reached from the statistical analysis performed. Special

attention should be paid to Cr and Ni, which, although they

seem to have a natural origin, demonstrate elevated values,

especially at the SW and SE parts of the Ptolemais–Kozani

basin. Finally, it seems that the combination of bivariate

analysis, multivariate statistics, and geostatistical (inter-

polation) methods may prove really useful in environ-

mental geochemical studies conducted in areas where fossil

fuels are used for energy production.

Acknowledgments The first author gratefully acknowledges the

assistance of Dr Kevin Clemitshaw and, lab technician, Sue Hall of

the Royal Holloway University of London, as well as the assistance of

Dr Christos Sachanidis of the Public Power Corporation of Greece

SA. The constructive comments of the anonymous reviewers that

helped improve the original manuscript are gratefully appreciated.

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