A Geostatistical Approach to Assess Concentrationand Spatial Distribution of Heavy Metals in Urban Soils

Ilaria Guagliardi & Domenico Cicchella &

Rosanna De Rosa

Received: 29 December 2011 /Accepted: 10 September 2012 /Published online: 27 September 2012# Springer Science+Business Media B.V. 2012

Abstract Characterization of spatial variation of heavymetals in urban soils is essential to identify pollutionsources and potential risks to humans and the environ-ment. While heavy metals concentration in soils dependsalso on the nature of bedrock and on abiotic and bioticfactors, it can be argued that nowadays, due to increasinghuman activities, it is determined mainly by anthropo-genic sources. We determined concentrations and spatialdistribution of heavy metals, with particular focus onthose potentially toxic (As, Cr, Pb, V, and Zn), in urbanand peri-urban soils of Cosenza-Rende (southern Italy).One hundred forty-nine samples of topsoil (0–10 cm)were collected and analyzed for 36 elements by X-rayfluorescence spectrometry and inductively coupled plas-mamass spectrometry (ICP-MS). In addition, 18 samplesof rocks were collected on outcrops of whole area and

analyzed by ICP-ES and ICP-MS. Geostatistical methodswere used to map the concentrations of major oxides andseveral minor elements. Heavy metals in the analyzedsamples showed a wide range of concentrations, primar-ily controlled by the geochemical composition of bed-rock, with the notable exceptions of Cu, Pb, and Zn,whose concentrations are heavily affected by land useand anthropogenic pollution in urban areas. Geochemicalanalysis and spatial distribution showed that high con-centrations of potentially toxic elements are found in soilsnear major roads, indicating that anthropogenic factorsdetermine the anomalies in these areas.

Keywords Soil . Heavymetals . Pollution .

Geochemistry .Multi-gaussian kriging

1 Introduction

With the growing demand for metals in industries andrapid urbanization in many parts of the world, con-tamination by metals in the terrestrial environment hasbecome widespread in a global context (Cicchella etal. 2008a, b, c; Lee et al. 2006; Li et al. 2009; Teng etal. 2010). However, studies of urban areas of Europeappear to be more abundant, probably a reflection ofthe fact that many cities of Europe have a longerindustrial history and hence a legacy of contamination(Johnson and Ander 2008). In many developingcountries, the expansion of urban areas is connectedwith (and often necessary for) socio-economic growth,

Water Air Soil Pollut (2012) 223:5983–5998DOI 10.1007/s11270-012-1333-z

I. Guagliardi (*)National Research Council of Italy, Institute for Agriculturaland Forest Systems in the Mediterranean (ISAFOM),Via Cavour 4-6,Rende, Cosenza, Italye-mail: ilaria.guagliardi@isafom.cnr.it

D. CicchellaDepartment of Biological, Geological and EnvironmentalSciences, University of Sannio,Via dei Mulini 59/A,82100 Benevento, Italy

R. De RosaDepartment of Earth Sciences, University of Calabria,Ponte Pietro Bucci,87036 Arcavacata di Rende, Cosenza, Italy

but it is however changing the physical, chemical, andbiological composition of living environment. As aconsequence, millions of people living in and aroundurban areas are exposed to an unnatural and unhealthyenvironment (Singh et al. 2002).

Soil plays a very important role in the food chain andhence is a very important pathway through with humansenter in contact with most pollutants, in particular heavymetals (Johnson and Ander 2008; Selinus et al. 2005),which have also been identified as co-factors in manydiseases (de Vries et al. 2008; Giaccio et al. 2012;Morton-Bermea et al. 2009).

Italian legislation fixes the intervention limits forboth organic and inorganic components in soils basedon local background values (Cicchella et al. 2005). Itis then of paramount importance to determine soilbackground values for potentially toxic elements(more than their baseline values) which are relevantin environmental studies. It is also very important todetermine the ratio of anthropogenic to lithogeniccontribution, even if this is quite difficult to assess inurban and industrialized areas where entirely unpol-luted soils are almost impossible to find (Romic andRomic 2003.)

Due to the aforementioned reasons, the extent ofthe interpretation that can be inferred from geochem-ical survey data is subject to a number of factors(Grunsky et al. 2009) which include spatial samplingdensity (i.e., the scale of the survey); knowledge of thelocal and regional geology; knowledge of analyticalprotocols including sample media, methods of diges-tion, and analytical instrumentation; and knowledge ofhuman activities (i.e., industry, agriculture, etc.) in thestudy area. Hence, studying soils in all its componentsas compartment of possible contamination is of ex-treme importance.

Soil science finds its historical roots in agriculture,geology, and chemistry, but with time and increasingknowledge, the emphasis shifted from classificationand inventory to understanding and quantifying spa-tially and temporally soil patterns related to ecosys-tems health (Grunwald 2009). There is thus a need toconnect the information from each soil to better un-derstand the complex bio-geochemical processes atthe regional scale (Salvador-Blanes et al. 2006). Forthis reason, geostatistical methods are now an essentialpart of the toolbox of most pedometricians and manysoil scientists (Larocque et al. 2006). Geostatisticsprovides an advanced methodology that facilitates

quantification of the spatial features of soil parametersand enables spatial interpolation (Shi et al. 2007). Inaddition, geostatistics has become a useful tool for thestudy of spatial uncertainty and hazard assessment(Goovaerts 1999). To determine geochemical concen-trations and spatial distribution of elements, variousstatistical methods are used including probabilitygraphs and univariate and multivariate analyses (Limaet al. 2008; De Vivo et al. 2008).

We used a multi-Gaussian approach, a recentlydeveloped technique described in detail by Verly(1983), to build geochemical maps of heavy metalsand other hazardous element (Al, Ag As, Ba, Be, Bi,B, Ca, Cd, Ce, Co, Cr, Cu, Fe, P, Y, La, Hg, Mg, Mn,Mo, Ni, Nb, Pb, K, Rb, Se, Si, Na, Sr, Tl, Te, Ti, V, Zn,Zr) distributions in the soils of Cosenza-Rende areas.

Our goals were to (1) define the heavy metalsbaseline in Cosenza-Rende soils, (2) indentify themost heavily polluted areas, (3) assess the contributionof parent materials and anthropogenic activity to geo-chemical baseline, and (4) identify the possible sour-ces of pollutants. In addition, baseline data for urbanareas of Cosenza-Rende can be used to assist policymakers and legislators to draw up a better legislationwith more appropriate guideline/intervention values.

2 Materials and Methods

2.1 The Study Area

The study area is in the NW sector of Calabria regioninside the graben of Crati Basin and covers an area of92 km2, with a population of approximately 106,000inhabitants.

The Crati Basin is the most important in theCalabria region both for its extension and for itsartificial lakes. It formed during the Pliocene, andso it is interested by some faults related to thehorst–graben system of the Sila-Coastal Chain(Lanzafame and Tortorici 1981). Cosenza andRende territories are inside the Crati valley whichis the largest flat area within the homonymousbasin. It is a tectonic depression NW orientedand located in axial position with respect to theApennine Chain, and it is bounded by faults. Geo-logically, this area is characterized by a powerfulsuccession of pliocenic sediments such as lightbrown and red sands and gravels, blue gray silty clays,

5984 Water Air Soil Pollut (2012) 223:5983–5998

and silt interlayers (Dell’Anna et al. 1981), overlappedon paleozoic intrusive-metamorphic complex formed byparagneiss, biotite schists, gray phyllitic schists withquartz, chlorite, and muscovite, which, in some cases,are in weathering process (Critelli et al. 1990; Le Peraand Sorriso-Valvo 2000; Le Pera et al. 2001) (Fig. 1).From a geomorphological point of view, the territory ischaracterized by two distinct zones. The first, where theurban center is located, is mostly flat, while the second ischaracterized by hilly terrain where the Crati, Jassa, andBusento rivers cut deeply in the crystalline substrate.The crystalline are rocks underwent intense physical–chemical weathering, which determined most of theirmorphological evolution. Climate is continental, char-acterized by large temperatures variations, both dailyand annually.

The Cosenza-Rende area is densely populated andcharacterized by typical urban land use, such as housing,intense automobile traffic, a limited presence of

industry, commercial activities, and parks and gardens.As in every typical urban area, several potential sourcesof pollution can be identified.

2.2 Sampling and Analytical Methods

The spatial sampling density, or scale, of a geo-chemical survey determines the resolution of thegeochemical features delineated from the resultingdata (Grunsky et al. 2009). In this study, we adoptedwell-established procedures for regional geochemicalmapping (Johnson and Ander 2008) to provide a sam-pling programme. It is generally accepted that the originand history of urban soils is conducive to a greaterheterogeneity than in nonurban areas; hence, samplingat a higher density is required to capture all possibleinformation. For this reason, the method of compositingsamples at a single site to give a more “representative”sample was adopted.

Fig. 1 Geological map ofCosenza-Rende territory

Water Air Soil Pollut (2012) 223:5983–5998 5985

The whole area was divided into cells of 1 by 1 kmin size in peri-urban areas and into cells of 0.5 by0.5 km in size in urban centre, and samples werecollected within a distance of 10 m surrounding eachsampling point and then were mixed. One hundredforty-nine samples were collected from 0–10 cm oftopsoil and flower beds and stored in polyethylenefilm bag after mixing for lab analysis. Each sampleweighed about 1 kg. Distribution of sampling points ispresented in Fig. 2. All samples were dried at 50 °C inan oven and sieved through a 2-mm mesh sieve. Thesefine fractions of soil samples was analyzed for 25elements by X-ray fluorescence spectrometry (XRF)(Al, As, Ba, Ca, Ce, Co, Cr, Fe, P, Y, La, Mg, Mn, Ni,Nb, Pb, K, Rb, Si, Na, Sr, Ti, V, Zn, Zr) and for 19elements by inductively coupled plasma mass spec-trometry (ICP-MS) (Ag, As, Be, Bi, B, Cd, Co, Cr,Hg, Mo, Ni, Pb, Cu, Se, Sr, Te, Tl, V, Zn). In the ICP-MS method, heavy metals and other elements wereextracted by the microwave technique in closed TFMvessels and with automatic pressure and temperatureregulation. After digestion with a mixed ternary con-centrated acid consisting of 6 ml of HClO4, 5 ml ofHF, and 3 ml of HNO3, elemental contents were de-termined according to the international standard meth-odologies. All soil samples were analyzed at theLaboratories of the Department of Earth Science of

the University of Calabria. Precision and accuracy ofthe analysis were calculated using reagent blanks,duplicate samples, and several referenced soil andsediment samples. Analytical precision was calculatedby the 20 % analysis (in duplicate) of randomly cho-sen samples and was found to be within accepted theinternational standards. Results were considered ac-ceptable when the coefficients of variation were within5 %. Certified international reference materials AGV-1, AGV-2, BCR-1, BCR-2, BR, DR-N, GA, GSP-1,GSP-2, SDC 1, QLO 1, and NIM-G (see http://georem.mpch-mainz.gwdg.de/ for chemical composi-tion) were used.

Eighteen samples of rocks were collected on out-crops in the whole area in order to determine naturalbackgrounds which are of paramount importance if onewants to fully understand the significance of the geo-chemical composition of the soils. Portions of the rocksamples were ground to fine particles using mechanicalagate grinder prior to analysis. The analyses of 18 rocksamples were carried out at Acme Analytical Laborato-ries, Ltd. (Vancouver, Canada), accredited under ISO9002, using Acme’s Groups 4A and 4B package. Totalabundances of the major oxides and several minor ele-ments are reported on a 0.2-g sample analyzed by ICPemission spectrometry following a lithium metaborate/tetraborate fusion and dilute nitric digestion. Loss onignition (LOI) is by weigh difference after ignition at1,000 °C. Rare earth and refractory elements were de-termined by ICPmass spectrometry following a Lithiummetaborate/tetraborate fusion and nitric acid digestionof a 0.2-g sample; in addition, a separate 0.5-g split isdigested in Aqua Regia and analyzed by ICP-MS toreport the precious and base metals.

Descriptive statistics, detection limits, accuracy,and precision for multi-elemental concentrations insoil and rock samples are reported in Tables 1 and 2.

2.3 Geostatistical Methods

The geostatistics approach consists of two parts. Thefirst is the calculation of an experimental variogramfrom the data and the model fitting, and the second isthe estimation or prediction at unsampled locations(Burgos et al. 2006). A variogram (or semi-variogram)is used to measure the spatial variability of a regional-ized variable and provides the input parameters for thespatial interpolation of variogram kriging (Webster andOliver 2001).Fig. 2 Soil sample locations

5986 Water Air Soil Pollut (2012) 223:5983–5998

Tab

le1

Sum

marystatisticsof

multi-elem

entalsoilconcentrations

Unit

Analytic

metho

dCou

ntMin

Max

Mean

Median

Low

erqu

artile

Upp

erqu

artile

SD

Skewness

Kurtosis

Detectio

nlim

it(D

L)

Accuracy

(%)

Precision

(RPD)

ICP-M

SXRF

Al

%–

✓14

911.19

23.79

15.89

15.38

13.65

17.48

2.8

0.69

0.04

0.03

3.5

0.9

Ca

%–

✓14

91.45

6.47

2.81

2.74

2.32

3.06

0.81

1.45

3.27

0.01

1.5

1.9

Fe

%–

✓14

93.11

10.58

5.47

5.13

4.42

6.16

1.47

1.03

1.01

0.04

3.9

1.8

P%

–✓

149

0.1

0.64

0.29

0.26

0.19

0.36

0.12

10.72

0.01

3.2

2.5

Mg

%–

✓14

91.45

6.47

2.81

2.74

2.32

3.06

0.81

1.45

3.27

0.01

1.5

1.9

Mn

%–

✓14

90.05

0.54

0.13

0.1

0.09

0.14

0.08

2.67

8.4

0.01

6.1

3.5

K%

–✓

149

1.32

3.45

2.41

2.4

2.22

2.6

0.34

0.06

1.28

0.04

11.3

Na

%–

✓14

90.44

2.08

1.23

1.23

1.02

1.46

0.34

0.03

0.31

0.01

3.8

1.6

Si

%–

✓14

933

.45

68.98

55.72

56.31

51.98

59.46

5.89

0.52

0.87

0.02

2.1

0.6

Ti

%–

✓14

90.45

1.18

0.73

0.71

0.61

0.83

0.15

0.47

0.2

0.01

4.6

2.5

Ag

mgkg

−1✓

–50

0.16

1.18

0.41

0.34

0.26

0.47

0.23

1.89

3.97

0.00

20.4

1.6

As

mgkg

−1✓

✓14

93

227.48

75

93.03

1.53

3.47

0.01

4.3

7.6

Ba

mgkg

−1✓

149

335

2000

603.05

592

530

643

153.08

5.36

46.74

0.05

6.7

2.7

Be

mgkg

−1✓

–50

1.51

5.64

2.67

2.5

2.26

2.93

0.66

2.09

7.25

0.18

1.3

2

Bi

mgkg

−1✓

–50

0.03

1.35

0.22

0.15

0.13

0.27

0.02

3.96

20.43

0.05

1.8

11.4

Bmgkg

−1✓

–50

3.17

44.65

10.21

7.64

5.53

11.14

8.31

2.71

8.29

0.92

10.4

6.6

Cd

mgkg

−1✓

–50

0.13

2.44

0.4

0.3

0.23

0.45

0.37

3.94

19.5

0.01

1.4

8

Ce

mgkg

−1✓

149

3412

772

.66

7060

8218

.77

0.82

0.57

0.05

8.2

11.9

Co

mgkg

−1✓

✓14

96

4017

.05

1613

205.91

1.21

2.02

0.05

8.6

10

Cr

mgkg

−1✓

✓14

946

309

90.54

8673

103

31.9

2.9

15.44

0.02

1.2

5.4

Ymgkg

−1✓

149

055

24.99

2619

308.15

0.35

1.08

0.01

6.9

4.7

La

mgkg

−1✓

149

1380

37.71

3731

4211.24

0.92

1.91

0.05

9.6

17.9

Hg

mgkg

−1✓

–50

0.09

0.45

0.19

0.18

0.13

0.25

0.09

1.18

1.41

0.01

1.3

1.2

Mo

mgkg

−1✓

–50

0.35

12.2

1.9

1.3

0.9

2.24

2.06

3.69

15.5

0.42

6.9

3.2

Ni

mgkg

−1✓

✓14

918

8234

.67

3328

409.93

1.16

2.95

0.01

8.1

3.8

Nb

mgkg

−1✓

149

635

14.49

1411

155.13

1.67

3.87

0.05

7.5

2.3

Pb

mgkg

−1✓

✓14

98

708

63.67

3120

6985

3.89

22.56

0.02

6.3

12.6

Cu

mgkg

−1✓

5011.59

249.97

44.36

30.38

20.92

38.26

45.75

2.96

9.75

0.27

5.9

9.7

Rb

mgkg

−1–

✓14

962

154

104.66

105

92114

18.15

0.33

0.2

0.05

5.4

1.6

Sr

mgkg

−1✓

✓14

910

951

423

4.25

233

194

271

63.73

0.63

1.66

0.05

4.2

1.4

Water Air Soil Pollut (2012) 223:5983–5998 5987

It can be expressed as:

gðhÞ ¼ 1

2NðhÞXNðhÞ

i¼1

ZðxiÞ � Zðxi þ hÞ½ �2 ð1Þ

where γ(h) is the semivariance at a given distance h, Z(xi) is the value of the variable Z at the xi location, andN(h) is the number of pairs of sample points separatedby the lag distance h. If the variogram rises and sta-bilizes around some value, then it has reached a sill(C) (i.e., theoretically the sample variance). If the vario-gram does not reach a sill, it might indicate that itcorresponds to phenomena with an unlimited capacityfor dispersion. The distance at which the variogramreaches the sill is called the range (a). Beyond thisdistance, data are independent.

Discontinuities at the variogram origin could bepresent. Such an unstructured component of variationat h00 is known as nugget effect (Co) which may bedue to sampling errors and short-scale variability. Thecalculation of the variogram is done in several orien-tations to assess if the spatial variability is the same inall directions. If it is, the distribution is called isotro-pic; otherwise, it is anisotropic.

After this stage, the variogram plot is fitted with atheoretical model (spherical, exponential, linear, orGaussian). It is possible to have a combination oftwo or more models (nested structure), with a totalvariance result of added variances of each model. Thebest fitting function can be chosen by cross-validation,which checks the compatibility between the data andthe model. The cross-validation consists of taking eachdata point, in turn removing it temporarily from thedata set and using its neighboring information to pre-dict the value of the variable at its location. Theestimate is compared with the measured value bycalculating the experimental error (i.e., the differencebetween estimate and measurement), which can alsobe standardized by estimating the standard deviation.The goodness of fit was first evaluated by the meanerror, which proves the unbiasedness of the estimate ifits value is close to 0:

Mean error ¼ 1

N

XN

i¼1

z* xið Þ � z xið Þ� � ð2Þ

where N is the number of observation points, z*(xi) isthe predicted value at location i, and z(xi) is the ob-served value at location i; the second is the meanT

able

1(con

tinued)

Unit

Analytic

metho

dCou

ntMin

Max

Mean

Median

Low

erqu

artile

Upp

erqu

artile

SD

Skewness

Kurtosis

Detectio

nlim

it(D

L)

Accuracy

(%)

Precision

(RPD)

ICP-M

SXRF

Tl

mgkg

−1✓

500.03

2.03

0.64

0.29

0.17

1.33

0.62

0.87

0.84

0.61

1.1

3.5

Vmgkg

−1✓

✓14

954

239

107.36

102

8712

330

.89

1.28

2.46

0.05

5.7

3

Zn

mgkg

−1✓

✓14

938

871

166.73

127

9318

913

0.53

2.68

8.7

0.01

5.4

2.8

Zr

mgkg

−1–

✓14

912

138

320

9.36

209

186

233

41.33

0.44

1.74

0.05

4.6

6.1

pH–

––

149

5.2

8.8

7.26

7.22

6.89

7.65

0.66

0.35

1.14

0.01

00.8

RPD

relativ

epercentdifference

5988 Water Air Soil Pollut (2012) 223:5983–5998

Tab

le2

Sum

marystatisticsof

multi-elem

entalrock

concentrations

Unit

Cou

ntMin

Max

Mean

Median

Low

erqu

artile

Upp

erqu

artile

SD

Skewness

Kurtosis

Detectio

nlim

it(D

L)

Accuracy(%

)Precision

(RPD)

Si

%18

0.29

91.16

56.65

57.37

46.23

75.13

26.32

0.96

0.41

0.01

0.03

0.89

Al

%18

0.05

19.53

10.38

10.39

8.29

13.80

5.42

0.39

0.11

0.01

0.02

3.47

Fe

%18

0.06

13.13

3.08

2.27

0.91

3.21

3.45

1.95

3.70

0.04

0.07

4.00

Mg

%18

0.12

22.57

2.52

0.87

0.36

1.64

5.24

3.68

14.44

0.01

0.15

0.00

Ca

%18

0.06

55.42

12.18

2.22

1.18

16.56

17.75

1.71

2.05

0.01

0.12

0.98

Na

%18

0.07

3.70

1.83

1.71

1.31

2.43

1.02

0.14

0.35

0.01

0.07

1.70

K%

180.02

6.35

2.34

2.01

1.11

3.71

1.80

0.51

0.28

0.01

0.12

5.28

Ti

%18

0.04

1.70

0.37

0.28

0.14

0.40

0.42

2.48

6.88

0.01

0.00

6.90

P%

180.02

0.17

0.08

0.08

0.06

0.10

0.04

0.56

0.97

0.01

0.00

16.67

Mn

%18

0.01

0.22

0.08

0.06

0.03

0.11

0.06

1.06

0.95

0.01

0.00

<0.01

Cr

%18

0.01

0.25

0.04

0.01

0.01

0.02

0.08

2.97

8.86

0.00

0.41

<0.00

2

Ni

mgkg

−118

26.00

159.00

77.50

62.50

27.50

112.50

63.57

0.75

1.81

20.00

3.25

2.09

Sc

mgkg

−118

1.00

33.00

7.93

6.00

3.50

8.00

7.97

2.54

7.32

1.00

2.88

40.00

Ba

mgkg

−118

5.00

2565

.00

634.06

454.00

280.00

853.25

615.19

1.93

5.02

1.00

1.36

4.22

Co

mgkg

−118

0.70

83.90

10.88

4.80

1.98

8.10

20.32

3.49

12.94

0.02

0.37

15.38

Cs

mgkg

−118

0.10

2.90

0.96

0.60

0.30

1.50

0.86

0.91

0.19

0.10

2.08

40.00

Ga

mgkg

−118

3.50

30.50

14.39

13.95

9.33

16.85

6.77

0.78

0.81

0.50

1.86

10.81

Hf

mgkg

−118

0.60

8.80

3.79

3.40

1.68

5.50

2.44

0.69

0.24

0.10

1.02

25.64

Nb

mgkg

−118

0.10

22.50

5.43

3.10

1.88

6.63

6.22

1.99

3.61

0.10

1.66

7.14

Rb

mgkg

−118

0.60

316.60

67.04

58.65

37.58

77.53

70.93

2.70

9.49

0.10

0.44

6.20

Sr

mgkg

−118

21.50

624.50

256.56

235.15

150.25

358.30

154.60

0.63

0.50

0.50

1.01

3.90

Ta

mgkg

−118

0.10

1.30

0.41

0.30

0.23

0.48

0.34

1.85

3.15

0.10

1.37

0.00

Th

mgkg

−118

0.40

58.70

12.22

6.75

4.53

9.38

15.72

2.33

5.09

0.20

3.13

2.90

Umgkg

−118

0.20

4.60

1.25

1.00

0.83

1.45

1.02

2.57

8.19

0.10

0.91

0.00

Vmgkg

−118

8.00

231.00

56.64

37.00

12.25

67.00

63.14

1.86

3.72

8.00

1.23

10.53

Wmgkg

−118

0.60

1.40

0.82

0.70

0.60

0.80

0.33

1.91

3.76

0.50

1.66

<0.5

Zr

mgkg

−118

0.40

281.10

113.63

111.20

41.55

163.08

88.10

0.55

0.54

0.10

0.91

9.02

Ymgkg

−118

0.70

49.90

15.44

11.70

7.13

20.88

13.20

1.35

1.66

0.10

1.42

12.31

La

mgkg

−118

0.40

109.90

26.69

19.05

8.78

26.50

29.07

1.95

3.51

0.10

0.83

6.27

Ce

mgkg

−118

0.60

228.00

52.69

35.45

16.50

50.90

60.08

2.01

3.72

0.10

1.43

2.66

Pr

mgkg

−118

0.08

27.39

6.53

4.25

2.22

6.61

7.25

1.94

3.40

0.02

0.15

6.28

Nd

mgkg

−118

0.40

103.60

25.07

15.80

9.85

25.58

27.43

1.94

3.34

0.20

0.36

1.62

Water Air Soil Pollut (2012) 223:5983–5998 5989

Tab

le2

(con

tinued)

Unit

Cou

ntMin

Max

Mean

Median

Low

erqu

artile

Upp

erqu

artile

SD

Skewness

Kurtosis

Detectio

nlim

it(D

L)

Accuracy(%

)Precision

(RPD)

Sm

mgkg

−118

0.07

17.64

4.48

3.03

1.94

4.51

4.67

1.88

3.06

0.05

2.41

6.94

Eu

mgkg

−118

0.02

2.35

1.03

0.87

0.68

1.37

0.67

0.60

0.08

0.02

1.18

0.52

Gd

mgkg

−118

0.08

11.25

3.42

2.15

1.74

3.80

3.07

1.63

2.32

0.05

1.04

8.40

Tb

mgkg

−118

0.02

1.43

0.52

0.43

0.28

0.58

0.41

1.17

0.66

0.01

1.96

8.00

Dy

mgkg

−118

0.05

7.85

2.55

1.90

1.22

3.48

2.02

1.22

1.58

0.05

1.87

6.06

Ho

mgkg

−118

0.03

1.78

0.54

0.42

0.21

0.66

0.46

1.56

2.44

0.02

2.82

4.88

Er

mgkg

−118

0.04

5.28

1.47

1.00

0.60

1.72

1.52

1.87

3.13

0.03

0.56

12.61

Tm

mgkg

−118

0.01

0.97

0.24

0.15

0.10

0.27

0.25

2.15

4.40

0.01

1.85

11.76

Yb

mgkg

−118

0.22

6.97

1.65

1.06

0.70

1.70

1.77

2.34

5.44

0.05

0.72

8.00

Lu

mgkg

−118

0.04

1.07

0.25

0.16

0.10

0.26

0.27

2.37

5.61

0.01

1.85

0.00

Mo

mgkg

−118

0.10

0.70

0.35

0.30

0.23

0.45

0.22

0.79

0.07

0.10

5.72

0.00

Cu

mgkg

−118

1.00

50.70

11.46

5.55

2.38

12.23

13.68

1.91

3.31

0.10

12.24

3.17

Pb

mgkg

−118

1.20

9.10

4.41

4.05

2.33

5.83

2.53

0.58

0.55

0.10

4.51

1.18

Zn

mgkg

−118

2.00

105.00

33.41

23.00

11.00

45.00

30.17

1.30

1.08

1.00

4.69

2.74

As

mgkg

−118

0.60

15.60

5.99

7.50

1.00

8.35

4.78

0.47

0.11

0.50

2.50

2.13

Au

mgkg

−118

0.50

3.20

1.79

1.80

1.10

2.60

0.88

0.24

−1.08

0.50

15.02

103.45

RPD

relativ

epercentdifference

5990 Water Air Soil Pollut (2012) 223:5983–5998

squared deviation ratio (MSDR), which is the ratiobetween the squared errors and the kriging variance(σ2(xi)):

MSDR ¼ 1

N

X1

i¼1

z*ðxiÞ � z xið Þ� �2

σ2 xið Þ ð3Þ

If the model for the variogram is accurate, the meansquared error should equal the kriging variance andthe MSDR value should be 1.

The fitted model provides information about thespatial structure as well as the input parameters forkriging interpolation (Burgos et al. 2006). In fact, afterselecting an appropriate variogram model, the param-eters can be used with the data to predict heavy metalconcentrations at unsampled locations using kriging.

Among the several estimation methods, kriging isthe most popular because it “… is a collection ofgeneralized linear regression techniques for minimiz-ing and estimating variance defined from a prior mod-el for a covariance” (Olea 1991). Kriging provides the“best,” unbiased, linear estimate of a regionalizedvariable in an unsampled location, where “best” isdefined in a least-squares sense (Webster and Oliver2001). There are numerous types of kriging algorithms(ordinary kriging, lognormal kriging, etc.) and theselection of a particular one should be guided primar-ily by the characteristic of the data under study (Saitoand Goovaerts 2000).

Log-normal kriging must be used with caution be-cause it is non-robust against departures from the log-normal model and the back-transform is very sensitiveto semivariogram fluctuations. Moreover, althoughlog-normal and normal-score transforms attenuate theimpact of high values, they are not appropriate forcensored data, which are better viewed as a differentstatistical population. We adopted the multi-Gaussiankriging to explore heavy metals concentration in thestudy area.

Multi-Gaussian kriging is an approach used to re-duce the influence of skewed distribution of data(Buttafuoco et al. 2007) and allows prediction atunsampled locations regardless of the shape of thesample histogram (Goovaerts 1997; Verly 1983).

The multi-Gaussian approach guarantees theoreti-cal access to the strict conditional probability density,thus providing a consistent solution. In addition, itsapplication does not require the use of orthogonalpolynomials and therefore is simpler to implement

than disjunctive kriging (Verly 1983). In the multi-Gaussian model, the attribute under study is viewedas a realization of a random function Z(x) that can betransformed into a Gaussian random field Y(x) withzero mean and unit variance:

ZðxÞ ¼ f Y ðxÞ½ � ð4ÞThe multi-Gaussian assumption implies that the dis-

tribution of any value Y(x) conditioned by sampledvalues is still Gaussian (Emery 2005a). Such a proce-dure is known as Gaussian anamorphosis (Wackernagel2003), and it is a mathematical function which trans-forms a variable Y with a Gaussian distribution in a newvariable with any distribution. To transform the rawvariable into a Gaussian one, we have to invert thisfunction:

Y ðxÞ ¼ f�1 ZðxÞ½ � ð5ÞThe main advantage of this approach is that the

Gaussian values obtained after the iterative algorithmare the same than the ones obtained by applying themodeled transformation function to the raw data, ashonors the empirical points (Emery and Ortiz 2005).

The Gaussian anamorphosis can be achieved usingan expansion into Hermite polynomials Hi(Y) restrictedto a finite number of terms:

fðY Þ ¼Xn

i¼0

y iHiðY Þ ð6Þ

The modeling of the anamorphosis starts with thediscrete version of the curve on the true data set (seeFig. 3); then, a model expanded in terms of Hermitepolynomials (Eq. 6) is fitted to the discretized anamor-phosis. This model gives the correspondence betweeneach one of the sorted raw data and the correspondingfrequency quantile in the standardized Gaussian scale.

In the multi-Gaussian approach, we can choicebetween simple and ordinary kriging. Unlike sim-ple multi-Gaussian kriging, as disjunctive kriging(Matheron 1976), ordinary multi-Gaussian krigingaccounts for the local mean calculated after the datalocated in the kriging neighborhood (Emery 2005b).The use of ordinary kriging in the multi-Gaussian ap-proach allows a weakening of the stationary assumptionwhen using non-linear geostatistical methods and makesit robust to a departure of the data from the ideal statio-narity model.

Water Air Soil Pollut (2012) 223:5983–5998 5991

The transformed data are used for interpolation atall unsampled locations as a linear combination of n(x0) Gaussian data surrounding the unsampled point(x0) using this expression:

y*MK x0ð Þ ¼Xn x0ð Þ

i¼1

lMKi xið Þy xið Þ ð7Þ

Finally, estimates and kriging variances are back-transformed using the Gaussian anamorphosis (Eq. 4);unlike lognormal kriging, this is the straight Gaussiananamorphosis without any need to correct for bias.

Environmental decision making is increasinglybased upon spatial variability procedures. The evidentreason is that national laws protecting the quality ofthe environment are defined in terms of quantitativenorms (Cui et al. 1995). For this reason, kriging is notjust used to estimate unsampled areas, it is also used tobuild probabilistic models of uncertainty about the un-known but estimated predicted values (Deutsch andJournel 1998).

Kriging estimates can be mapped, to reveal theoverall trend of data. Maps provide helpful visualdisplays of the spatial variability in the field andcan be used for the summarization and represen-tation of soil properties where natural hazards canbe identified (Goodchild et al. 1993). So, thegeochemical maps can prove to be very usefulboth for scientists and professionals working to-wards the restoration of contaminated areas, aswell as serving to refine the Italian environmental leg-islation to be more effective in local situations (Cic-chella et al. 2008b).

3 Results and Discussion

Concentrations of heavy metal and the other hazard-ous elements (milligrams per kilogram, dry weightbasis) in soils ranged spatially from 3.0 to 22.0 forAs, 6.0 to 40.0 for Co, 46.0 to 309.0 for Cr, 18.0 to82.0 for Ni, 8.0 to 708.0 for Pb, 54.0 to 239.0 for V,38.0 to 871.0 for Zn (data resulting from XRF analy-sis), 1.51 to 5.64 for Be, 0.13 to 2.44 for Cd, 11.59 to249.97 for Cu, 0.09 to 0.45 for Hg, and 0.03 to 2.03for Tl (data resulting from ICP-MS analysis) (Table 1).

Considering the narrowness of space, where notall variable maps can be represented, the choicewas for elements harmful to soil environment andhuman health, and so only arsenic (As), chromium(Cr), lead (Pb), vanadium (V), and zinc (Zn) wereselected to represent the geochemical environmentof Cosenza-Rende.

The histograms of the As, Cr, Pb, V, and Zn con-centrations were characterized by a positively skeweddistribution, and the data did not follow a normaldistribution.

An example of result of the Gaussian anamorpho-sis was given in Fig. 3, where the transition fromraw to Gaussian data is represented in Pb histo-grams. In the central image of Fig. 3, the thin lineis the experimental anamorphosis and the thick lineis the fitted model inside the bounds of the intervalof definition (horizontal dotted lines). The bound-aries of the interval are the experimental minimumand maximum within which the function is mono-tonically increasing. The interpolated maps of soilheavy metals, computed using Isatis® version 10(http://www.geovariances.com), and respective krig-ing standard deviation are shown in Fig. 4.

Fig. 3 Example of the experimental anamorphosis graph for Pb with raw data and normal scores histograms

5992 Water Air Soil Pollut (2012) 223:5983–5998

The goodness of fitting model was verified by crossvalidation, and the results were quite satisfactory be-cause the statistics used (i.e., mean of the estimationerror and variance of the mean squared deviation ratio)were quite close to 0 and 1, respectively.

The spatial variability of soil attributes can be affect-ed by both soil pedogenetic factors (such as parentmaterial) and human activities (such as vehicular traf-fic). The high values of Co, Cr, Fe, Mg, and V can be

explained by the occurrence in the study area of theultrabasic rocks in which these elements are predomi-nant. Highest baseline values of these elements appearto be highly correlated to the igneous–metamorphiccomplex found below the Pliocene deposits but thatoutcrops mostly in the NW and SE sectors of the terri-tory. The complex can be ascribed to the pile of tectonicnappes forming the mountain chain of the northernCalabrian Arc, described in Tortorici et al. (2009), and

Fig. 4 Interpolated data distribution for As (a), Cr (c), Pb (e), V (g), and Zn (i) and their respective kriging standard deviation (b, d, f,h, and j)

Water Air Soil Pollut (2012) 223:5983–5998 5993

Fig. 4 (continued)

5994 Water Air Soil Pollut (2012) 223:5983–5998

Fig. 5 Correlation diagrams between soil (solid diamond) and rock (solid square) heavy metal concentrations

Water Air Soil Pollut (2012) 223:5983–5998 5995

includes an intermediate structural element made up ofophiolite-bearing units that mostly extend alongthe Tyrrhenian side of the arc to form a westwardconvex arc-shaped belt separated from the southernApennines by the roughly E–W trending left-lateralstrike-slip fault zone, the Sangineto line in AmodioMorelli et al. (1976).

This unit is represented by a tectonic mélange con-stituted by a monotonous sequence of phyllites,quartzites, and calcschists including metric to kilomet-ric lens-shaped blocks of ophiolitic rocks. These rocksare mainly represented by serpentinized ultramaficsand by glaucophane-bearing meta-basites with rem-nants of their sedimentary cover and rare meta-gabbros(Tortorici et al. 2009).

Quite often, lack of information on the geo-chemical composition of bedrocks leads to adoptelemental concentrations internationally deter-mined, typical of analyzed rocks but from otherworld areas, to identify natural background. Thiscauses a high degree of imprecision in the defini-tion of the background, and results in adverseconsequences on the correct estimation of the geo-chemical anomalies.

To overcome this problem, we analyzed the majorand trace elemental concentrations of the Cosenza-Rende bedrocks. Concentrations of heavy metal and

other hazardous elements (milligrams per kilogram,dry weight basis) in rocks ranged spatially from 0.60to 15.60 for As, 1.00 to 3.00 for Be, 0.10 to 0.20 forCd, 0.70 to 87.90 for Co, 0.01 to 0.25 for Cr, 1.00 to50.70 for Cu, 0.50 to 108.30 for Ni, 1.20 to 9.10 forPb, 0.10 to 1.20 for Tl, 8.00 to 231.00 for V, and 2.00to 105.00 for Zn (Table 2).

Data were also examined using correlations dia-grams (Fig. 5) in which concentrations of heavy met-als in soils were compared with the mean localcomposition of rocks of the whole area. This compar-ison allowed us to identify the contributions of differ-ent lithotypes in the geochemistry of each soil sampleand locate possible anthropogenic anomalies for someelements shown by soil samples which were locatedoutside the compositional range defined by the meanlocal composition of outcrops.

In these diagrams, the silica oxide was chosen asreference variable because the main local lithotypeswere characterized by distinct content of this oxide,with a strong contrast between marble (0.29 % inweight) and gneiss (91.16 %). Graphs were distin-guished by the two analytical techniques used, XRFand ICP-MS.

When compared with the background values,many soil samples fell within the compositionalfield defined by the local content of marbles, sand-stones, and gneisses, suggesting a strong litholog-ical control on elements concentrations. This wasthe case of As, Ba, Ca, Co, Fe,Mg, Na, K, La, Rb, Sr, Ti,V, Y, and Zr, which proved to be of undoubtedly ofgeogenic origin.

Conversely, urban and peri-urban soils wereenriched with metals such as Cd, Cu, Mn, Mo,Ni, P, Pb, Tl, and Zn. They were placed outsidethe rock compositional field of study area becauseheavy metal concentrations of urban soils weregenerally higher than those in peri-urban soils,for the anthropogenic activities which took place inurban environments.

Thus most of peri-urban unpolluted areas had highAs, Cr, and V baseline values, which can be assumedto represent the natural background for outcrops.

The maps show very clearly that sites with thehighest concentrations of Cu, Pb, and Zn arefound where human activities are more intensesuch as the main roads (Fig. 6) and urban areas,all locations of anthropogenic sources of pollutionaround the city. In particular, the highest Pb and

Fig. 6 Roads map

5996 Water Air Soil Pollut (2012) 223:5983–5998

Zn baseline values in soils were found in closecorrespondence to high traffic roads of the Cosenza-Rende area. These elements are present in vehiclefuel since they are used in order to raise thegasoline antiknock.

4 Conclusions

A comprehensive soil survey was conducted to inves-tigate heavy metal contents and their relationshipswith geogenic and anthropogenic ratio in Cosenza-Rende area, and to evaluate the usefulness of an inno-vative geostatistical approach for mapping their geo-chemical concentrations and spatial distributions suchas multi-Gaussian kriging. These baseline dataallowed us to identify hot spots (those large enoughto be identified at the sample density of the currentsurvey) and areas that may pose potential risks toresidents of Cosenza-Rende.

Multi-Gaussian kriging and geographical informa-tion system were used to assess the degree of heavymetal contamination in soils of urban and peri-urbanareas of Cosenza-Rende. The multi-Gaussian krigingwas found to be a helpful method in soil scienceapplication concerned with spatial distribution andgeochemical mapping, better than other types of krig-ing such as log-normal kriging, indicator kriging, etc.Unlike the log-normal transform, multi-Gaussiantransform allows one to symmetrize the distributionof data regardless of the shape of the sample histo-gram. It was emphasized that geochemical maps gen-erated by multi-Gaussian kriging for environmentalpurposes show a realistic data distribution reflectingin detail the geolithological formations. Using thispowerful tool, we identified the presence of someanomalies by anthropogenic source in urban soils,concerning elements such as Pb and Zn, and of somegeogenic anomalous abundances of As, Cr, and Vmainly found in peri-urban areas. However, in gen-eral, urban soils were found to be more contaminatedthan peri-urban soils.

Acknowledgments The quality of this study was greatly en-hanced by the cooperation and interest of Dr Gabriele Buttafuoco,researcher of National Research Council of Italy—Institute forAgricultural and Forest Systems in the Mediterranean(ISAFOM), whose advices on geostatistics and help with

subsequent analysis were invaluable. The authors thank Dr. LucaFedele (Virginia Tech) for valuable comments and languageediting.

References

Amodio Morelli, L., Bonardi, G., Colonna, V., Dietrich, D.,Giunta, G., Ippolito, F., et al. (1976). L’arco calabro-peloritano nell’orogene appenninico-maghrebide.Memoriedella Società Geologica Italiana, 17, 1–60.

Burgos, P., Madejón, E., Pérez-de-Mora, A., & Cabrera, F. (2006).Spatial variability of the chemical characteristics of a trace-element-contaminated soil before and after remediation.Geoderma, 130, 157–175.

Buttafuoco, G., Tallarico, A., & Falcone, G. (2007). Mappingsoil gas radon concentration: a comparative study of geo-statistical methods. Environmental Monitoring and Assess-ment, 131, 135–151.

Cicchella, D., De Vivo, B., & Lima, A. (2005). Background andbaseline concentration values of elements harmful to hu-man health in the volcanic soils of the metropolitan andprovincial areas of Napoli (Italy). Geochemistry: Explora-tion, Environment, Analysis, 5, 29–40.

Cicchella, D., De Vivo, B., Lima, A., Albanese, S., Mc Gill, R.A. R., & Parrish, R. R. (2008a). Heavy metal pollution andPb isotopes in urban soils of Napoli, Italy. Geochemistry:Exploration, Environment, Analysis, 8, 103–112.

Cicchella, D., De Vivo, B., Lima, A., Albanese, S., & Fedele, L.(2008b). Urban geochemical mapping in the Campaniaregion (Italy). Geochemistry: Exploration, Environment,Analysis, 8, 19–29.

Cicchella, D., Fedele, L., De Vivo, B., Albanese, S., & Lima, A.(2008c). Platinum group element distribution in the soilsfrom urban areas of Campania Region (Italy). Geochemis-try: Exploration, Environment, Analysis, 8, 31–40.

Critelli, S., Gullà, G., Matano, F. (1990). L’alterazione dellerocce cristalline e sua incidenza sulla franosità in Calabria.In Antronico L., Critelli S., Gabriele S., Versace P. (Eds),Indagine a scala regionale sul dissesto idrogeologico inCalabria provocato dalle piogge dell’inverno 1990 (pp.51–73) .CNR-GNDCI Vol. spec. Editoriale BIOS Cosenza.

Cui, H., Stein, A., & Myers, D. E. (1995). Extension of spatialinformation, bayesian kriging and updating of prior vario-gram parameters. Environmetrics, 6, 373–384.

de Vries, W., Römkens, P. F. A. M., & Bonten, L. T. C. (2008).Spatially explicit integrated risk assessment of present soilconcentrations of cadmium, lead, copper and zinc in TheNetherlands. Water, Air, and Soil Pollution, 191, 199–215.

Dell’Anna, L., Rizzo, V., & Simone, A. (1981). Composizionemineralogica e granulometrica e alcune caratteristiche geo-tecniche delle argille infraplioceniche della Media Valle delFiume Crati. Caratteri distintivi delle argille in frana. Ren-diconti Società Italiana di Mineralogia e Petrologia, 37,161–178.

De Vivo, B., Lima, A., Bove, M. A., Albanese, S., Cicchella, D.,Sabatini, G., et al. (2008). Environmental geochemicalmaps of Italy from the FOREGS database. Geochemistry:Exploration, Environment, Analysis, 8, 267–277.

Water Air Soil Pollut (2012) 223:5983–5998 5997

Deutsch, C. V., & Journel, A. G. (1998). GSLIB: geostatisticalsoftware library and user’s guide. New York: Oxford Univer-sity Press.

Emery, X. (2005a). Multigaussian kriging for point-supportestimation: incorporating constraints on the sum of thekriging weights. Stochastic Environmental Research andRisk Assessment, 20, 53–65.

Emery, X. (2005b). Simple and ordinary multigaussian kriging forestimating recoverable reserves. Mathematical Geology, 37,295–319.

Emery, X., & Ortiz, J. M. (2005). Histogram and variograminference in the multigaussian model. Stochastic Environ-mental Research and Risk Assessment, 19, 48–58.

Giaccio, L., Cicchella, D., DeVivo, B., Lombardi, G., & De Rosa,M. (2012). Does heavy metal pollution affects semen qualityin men? A case of study in the metropolitan area of Naples(Italy). Journal of Geochemical Exploration, 112, 218–225.

Goodchild, M. F., Parks, B. O., & Steyaret, L. T. (1993).Environmental modelling with GIS. New York: OxfordUniversity Press.

Goovaerts, P. (1997). Geostatistics for natural resources evalu-ation. New York: Oxford University Press.

Goovaerts, P. (1999). Geostatistics in soil science: state-of-the-art and perspectives. Geoderma, 89, 1–45.

Grunsky, E. C., Drew, L. J., David, M., & Sutphin, D. M.(2009). Process recognition in multi-element soil andstream-sediment geochemical data. Applied Geochemistry,24, 1602–1616.

Grunwald, S. (2009). Multi-criteria characterization of recent dig-ital soil mapping and modeling approaches. Geoderma, 152,195–207.

Johnson, C. C., & Ander, E. L. (2008). Urban geochemicalmapping studies: how and why we do them. EnvironmentalGeochemistry and Health, 30, 511–530.

Lanzafame, G., & Tortorici, L. (1981). La tettonica recente dellaValle del Fiume Crati (Calabria). Geografia Fisica e Dina-mica Quaternaria, 4, 11–21.

Larocque, G., Dutilleul, P., Pelletier, B., & Fyles, J. W. (2006).Conditional Gaussian co-simulation of regionalized com-ponents of soil variation. Geoderma, 134, 1–16.

Le Pera, E., & Sorriso-Valvo, M. (2000). Weathering and mor-phogenesis in a mediterranean climate, Calabria, Italy.Geomorphology, 34, 251–270.

Le Pera, E., Critelli, S., & Sorriso-Valvo, M. (2001). Weatheringof gneiss in Calabria, southern Italy. Catena, 42, 1–15.

Lee, C. S., Li, X., Shi, W., Cheung, S. C., & Thornton, I. (2006).Metal contamination in urban, suburban, and country parksoils of Hong Kong: a study based on GIS and multivariatestatistics. The Science of the Total Environment, 356, 45–61.

Li, P., Wang, X., Zhang, T., Zhou, D., & He, Y. (2009). Distribu-tion and accumulation of copper and cadmium in soil–ricesystem as affected by soil amendments. Water, Air, and SoilPollution, 196, 29–40.

Lima, A., Plant, J. A., De Vivo, B., Tarvainen, T., Albanese, S.,& Cicchella, D. (2008). Interpolation methods for geo-chemical maps: a comparative study using arsenic datafrom European stream waters. Geochemistry: Exploration,Environment, Analysis, 8, 41–48.

Matheron, G. (1976). A simple substitute for conditional expec-tation: the disjunctive kriging. In M. Guarascio, et al.(Eds.), Advanced geostatistics in the mining industry (pp.221–236). Proceedings of NATO A.S.I. Dordrecht: Reidel.

Morton-Bermea, O., Hernández-Álvarez, E., González-Hernández,G., Romero, F., Lozano, R., & Beramendi-Orosco, L. E.(2009). Assessment of heavy metal pollution in urban topsoilsfrom the metropolitan area of Mexico City. Journal of Geo-chemical Exploration, 101, 218–224.

Olea, R. (1991). Geostatistical glossary and multilingual dic-tionary. New York: Oxford University Press.

Romic, M., & Romic, D. (2003). Heavy metals distribution inagricultural topsoils in urban area. Environmental Geology,43, 795–805.

Saito, H., & Goovaerts, P. (2000). Geostatistical interpolation ofpositively skewed and censored data in a dioxin-contaminated sites. Environmental Science and Technology,34, 4228–4235.

Salvador-Blanes, S., Cornu, S., Bourennane, H., & King, D.(2006). Controls of the spatial variability of Cr concentra-tion in topsoils of a central French landscape. Geoderma,132, 143–157.

Selinus, O., Alloway, B., Centeno, J. A., Finkelman, R. B.,Fuge, R., Lindh, U., et al. (2005). Essentials of medicalgeology. Impacts of the natural environment on publichealth. Amsterdam: Elsevier.

Shi, J., Wang, H., Xu, J., Wu, J., Liu, X., Zhu, H., et al.(2007). Spatial distribution of heavy metals in soils: acase study of Changxing, China. Environmental Geology,52, 1–10.

Singh, M., Müller, G., & Singh, I. B. (2002). Heavy metals infreshly deposited stream sediments of rivers associatedwith urbanisation of the Ganga Plain, India. Water, Air,and Soil Pollution, 141, 35–54.

Teng, Y., Ni, S., Wang, J., Zuo, R., & Yang, J. (2010). Ageochemical survey of trace elements in agricultural andnon-agricultural topsoil in Dexing area, China. Journal ofGeochemical Exploration, 104, 118–127.

Tortorici, L., Catalano, S., &Monaco, C. (2009). Ophiolite-bearingmélanges in southern Italy. Geological Journal, 44, 153–166.

Verly, G. (1983). The multigaussian approach and its applicationto the estimation of local reserves. Mathematical Geology,15, 259–286.

Wackernagel, H. (2003).Multivariate geostatistics: an introduc-tion with applications. Berlin Heidelberg New York:Springer.

Webster, R., & Oliver, M. A. (2001). Geostatistics for environ-mental scientists. Chichester: Wiley.

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