risk based characterisation of contaminated industrial...

Risk based characterisation of contaminated industrial site usingmultivariate and geostatistical tools

C. Carlon a, A. Critto a, A. Marcomini a,*, P. Nathanail b

aEnvironmental Sciences Department, University of Venice, Calle Larga S. Marta 2137, 30123 Venice, ItalybLand Quality Management, School of Chemical Environmental and Mining Engineering, University of Nottingham, Nottingham, UK

Received 10 May 1999; accepted 15 February 2000

``Capsule'': Kriging and principal component analysis were useful in obtaining additional information from data sets.

Abstract

Human and ecological risk assessment requires the sources, distribution, mobility and environmental behaviour of contaminantsto be investigated on a site-speci®c basis. It often deals with data sets which are relatively small and a�ected by sampling gaps. In

the case of a polycyclic aromatic hydrocarbon (PAH) contaminated industrial site, Kriging interpolation of spatial data and prin-cipal component analysis (PCA) proved useful for extracting additional value from the data set. Kriging was adopted for assessingthe horizontal and vertical distribution and transport of PAHs in soil. PCA was applied to PAH concentration and relative abun-

dance in soil samples and interpreted on the basis of the PAH physico-chemical and bio-degradation properties. It revealed corre-lation with the products of a neighbouring factory and the weathering of the lighter PAHs. The geo- and multivariate statisticalresults were coupled with the previous hydrogeological characterisation of the site to develop a site-conceptual model for use in the

exposure scenario modelling for risk assessment. # 2000 Elsevier Science Ltd. All rights reserved.

Keywords: PAHs; Risk assessment; Soil contamination; Multivariate statistics; Geostatistics

1. Introduction

Risk assessment has been internationally recognised asthe most cost-e�ective and scienti®c tool for tackling theoverwhelming problem of the contaminated sites man-agement (US-EPA, 1989; CARACAS and NICOLE,1997; Ferguson and Kasamas, 1999). In terms of humanhealth, risk assessment involves identifying the potentialfor adverse health e�ects to be caused by chemicals ofconcern from a site, and thereby determine the need forremedial action or the development of target levelswhere remedial action is required.Risk assessment procedures are generally based on

the source±pathway±receptor model (US-EPA, 1989;ASTM, 1995; CONCAWE, 1997), and encompass theexamination of the site characteristics, the environ-mental behaviour and toxicity of the contaminants, thepotential route of entry of the contaminants into the

receptors (humans), the exposure of the receptors to thecontaminants and their response to the dose.Thus, site characterisation is the basis for risk assess-

ment. Although much scienti®c literature is developing onrisk assessment issues (Ferguson, 1996), comparativelylittle attention is paid to the characterisation.A proper risk-oriented characterisation should pro-

vide a conceptual model of the site, a quali/quantitativerepresentation of the contaminant sources and as muchof the data necessary for modelling contaminant fateand transport (Ferguson et al., 1998). The identi®cationof both primary and secondary sources is generallyrecommended (ASTM, 1995). The primary source is thecause of the actual contamination, and concerns the natureand the place of the discharge, the mechanisms of trans-port and the environmental processes occurred, whereasthe secondary source is the impacted environmental mediato which the receptor is exposed.Since risk assessment is frequently a tiered or phased

approach,moving from conservative assumptions tomoresite-speci®c and accurate characterisations, the character-isation is also a tiered approach, based on preliminary

0269-7491/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.

PI I : S0269-7491(00 )00089-0

Environmental Pollution 111 (2001) 417±427

www.elsevier.com/locate/envpol

* Corresponding author. Tel.:+33-41-2578690; fax:+33-41-2578584.

E-mail address: [email protected] (A. Marcomini).

through to detailed investigations. The selection of theappropriate level of detail necessary for risk assessmentdepends on the complexity and the particular circum-stances of the site, as well as cost and other projectconstraints (US-EPA, 1990).Both the characterisation and the overall risk assess-

ment, have to deal with a large number of uncertainties(Dakins et al., 1994). Due to the heterogeneity of thesoil and the often accidental nature of contaminatingprocesses, concentrations of pollutants may varyremarkably over very short distances. This often makesit di�cult to obtain a meaningful picture of the con-tamination and to develop a conceptual model of thesite. Moreover, it is common and often unavoidable tohave to deal with small data sets and not exhaustivesamplings in the horizontal and vertical dimension. Theelimination of uncertainty is not feasible, cost-e�ectiveor necessary for estimating the risk and selectingappropriate remedies. The need to quantify and reducethe uncertainties and minimise the investigation costs,strongly encourages the use of geostatistical and multi-variate statistical methods (Ferguson, 1998; Ferguson etal., 1998). Kriging and principal component analysis(PCA) are two common examples of geostatistical andmultivariate statistical methods, respectively.Kriging is a linear-weighted gridding method, that has

been already successfully used to produce illustrativecontour plots of contaminant distribution on the basisof scattered observed concentration data (Leonte andScho®eld, 1996; Juang and Lee, 1998; Nathanail et al.,1998). It generally allows one to increase the informa-tion retrieval from analytical data and thereby reducesinvestigation costs.PCA has been extensively applied in many disciplines,

but not yet in the risk assessment-oriented characterisa-tion of contaminated sites. PCA enables the pollutantcomposition in di�erent samples to be compared andalso provides ®ngerprints for identifying the origin ofthe pollution (Burns et al., 1997).The objective of this work is to extend, by the use of

Kriging, PCA and additional data, the results of therisk-based characterisation of an industrial con-taminated site presented by Carlon et al. (2000). Thissite, located near Fidenza (Parma, Italy), was selected asa case study in a project co-ordinated by the NationalEnvironmental Protection Agency of Italy (ANPA) fordetermining risk assessment guideline values. The char-acterisation of the site included analytical survey ofboth soil and groundwater. On the basis of the formerindustrial use of the site and the results of previous sur-veys, the sampling strategy focused on de®ning theextent of both total and tetraethyl Pb contamination insoil. However, the soil analyses also showed signi®cantpolycyclic aromatic hydrocarbon (PAH) contamination.A preliminary risk assessment referred to the possiblecommercial use of the site outlined a signi®cant risk for

human health derived from Pb and tetraethyl lead(Et4Pb) through dust ingestion and vapour inhalation,and only from benzo(a)pyrene, benzo(a)anthracene andnaphthalene through leaching to groundwater.Previous results suggested the need for further inves-

tigation of PAH distribution and the relation of thePAH contamination in the study area with that origi-nated from a factory producing PAHs located on theEast site.The use of Kriging and PCA as explorative methods to

describe the distribution and identify the primary and sec-ondary sources of the site contamination is reported below.

2. The study area

The study area was described in detail by Carlon et al.(2000) and only the key features and additional detailsare present here. The study area is located in the indus-trial district North of Fidenza (Parma, Italy), and cov-ers some 3 km2 out of which approximately one-third isoccupied by disused factory buildings. Until 1976 thisarea was occupied by a factory (called CIP in this paper)producing Et4Pb, prior to being abandoned. To the eastthe study area adjoins a factory (called CARB in thispaper) producing PAHs (Fig.1), which has been heavilypolluting the soil (Ambert et al., 1995).The upper 1.5 m of soil appears disturbed and

permeable, while the subsurface unsaturated soil (1.5±4m layer) is a silty clayey layer including silty sandy hor-izons. A phreatic aquifer is settled in sandy-silt levelsand perched on clayey intercalations, at a depth of 4±9m, whereas a semicon®ned aquifer, partially connectedto the phreatic, is settled in a gravelly layer, at a depthof 10±25 m (Ambert, 1995; ANPA et al., 1998).According to previous measurements (ANPA et al.,1998), the phreatic watertable undergoes wide daily¯uctuations and sometimes reaches the 0.5 m top soil.

Fig. 1. Location of the study area (CIP), and of the adjacent factory

(CARB) producing polycyclic aromatic hydrocarbons (PAHs), in the

industrial district north of Fidenza.

418 C. Carlon et al. / Environmental Pollution 111 (2001) 417±427

Both the phreatic and the semicon®ned water table¯ow to the north-east with a low hydraulic gradient(0.15±1%). However, until the 1980s both aquifers wereheavily exploited for industrial purposes by several wellsin the CIP and surroundings areas (ANPA et al., 1998).Thus, it can reasonably be expected that, at that time,pumping from wells produced local deviations of thephreatic and semicon®ned ¯ows and, possibly, hydraulicconnections between aquifers.Soil analysis showed heavy contamination by total

and Et4Pb, distributed mainly in the vicinity of two set-tling tanks, on the west side of the area, (Fig.1), andcon®ned up to 1.5 m deep (the mean concentrations fortotal Pb and Et4Pb were ca. 19 and 2 g/kg dry wt,respectively). The PAHs were not expected to be foundin the study area, since they were not used by the CIPfactory. However the soil exhibited signi®cant PAHcontamination distributed mainly in the vicinity of thesettling tanks (naphthalene and benzo(a)pyrene were upto 1800 and 36 mg/kg dry wt, respectively).The semicon®ned aquifer in the CIP area was found to

be about one order of magnitude more contaminated thanthe phreatic aquifer by PAHs, with themore soluble PAHs(19.3 mg/l of naphthalene, 1.3 mg/l of methylnaphthalene)dominating over the others (<1mg/l). Almost puremineraloil was pumpedo� in the semicon®ned aquifer close to theCARB factory.

3. Material and methods

3.1. Analytical methods

The results of 46 soil samples collected in the CIP area(18 sampling points labelled with 1±5 and E3±14) andanalysed using the mobile chemical laboratory of theJoint European Research Centre (JRC) in 1996 (Carlonet al., 2000), were combined with those of 25 soil samplescollected in the east CIP area and CARB area (six sam-pling points labelled Car5, Car7, Car9, Car10, Car14 andCar16) and analysed by the CARB laboratory in 1995(Ambert et al., 1995). The sampling methods used in thetwo campaigns were very similar. In both cases a mobiledrilling rig was used, recovering soil cores (100 mm é)down to about 7 m depth. Samples were taken from themore permeable levels.On the mobile laboratory, the unsubstituted PAHs in

soil were extracted by a 1:1 (v/v) acetone±ethane mix-ture and determined by gas chromatography±massspectrometry (Fisons GC8000 gas chromatograph withAS800 autosampler). The sensitivity varied from 0.1 to1 mg/kg for each PAH. A detailed description of theanalytical procedure is reported elsewhere (Carlon et al,2000). In the CARB laboratory, the analysis of unsub-stituted PAHs was carried out by gas chromatography(HP 5790) coupled with a ¯ame ionisation detector.

Sample aliquots (100 g) were extracted after the addi-tion of 200 ml of distilled water and 50 ml of analyticalgrade n-pentane, followed by 3 h of stirring with amagnetic anchor and 1 h of ultrasonic bathing. Thesample extract was injected onto a HP fused silicacapillary column (crosslinked 5% phenilmethylsiliconeID 0.2 mm). Durene was used as an internal standard.The obtained sensitivity was around 10 mg/kg.Although the analytical sensitivity in the CARB

laboratories was lower than that obtained in the mobilechemical laboratory, CARB results were considered com-parable to those of JRC, because PAH concentrations inCARB soil were one to three orders of magnitude higherthan those in CIP soil.

3.2. Geostatistical and multivariate statistical methods

Kriging was used to interpolate concentration values.Then, on the basis of Kriging extrapolated values, con-tour plots of the contaminant distribution in the studiedarea were produced.The Kriging weights are calculatedby means of a functional model (variogram) describingthe spatial correlation of the data. The appropriatefunctional model is selected on the basis of the experi-mental variogram of the observed data, Zj, which iscalculated as follows:

j�h� � Var�Zj�x� h� ÿ Zj�x��;

where gj is the variance and h is the distance betweenlocations x+h and x.The variogram is summarised by three characteristics:

1. Sill, the plateau the variogram reaches.2. Range, the distance at which the variogram reaches

the sill, which represents the `range of correlation'of data values.

3. Nugget e�ect, the vertical height of the discontinuityat the origin, which represents the combination ofshort-scale variations and sampling-analyticalerrors.

Details of this method are provided elsewhere (Isaaksand Srivastava, 1989).In this study, the variogram functions of the experi-

mental data were computed and modelled by Variowin2.2 (Pannatier, 1996), while the Kriging and contourplot derivation were performed using Surfer 6.03(Golden Software Inc., 1996).Since the distribution of PAH concentrations showed

a log-normal distribution, the PAH concentrations werelog-trasformed to show a better agreement with a nor-mal distribution. It provided more regular variograms.The Kriging interpolations were performed on log-con-centrations and the estimated values were inverselytransformed by the exponential function.

C. Carlon et al. / Environmental Pollution 111 (2001) 417±427 419

The PCA was used to determine the variation of PAHcomposition in di�erent soil samples. PCA is a multi-variate statistical analysis converting the variables(analytes concentrations) in the so-called factors orprincipal components, i.e. linear combinations of thevariables that are themselves not correlated and toge-ther explain the total variance of the data. The ®rstfactor explains the most variance, the second factor thenext highest variance and so on. It follows that thedimensionality of the original data space can be reducedto a few factors, commonly two or three, retainingalmost all the system variance.Furthermore, the factors can be rotated, in a way that

each factor mostly explains a di�erent subset of corre-lated variables (i.e. analytes), making the factors morecomprehensive and causally explicable. The PCA allows`a factor score' for each sample to be calculated. Whenplotted by factor scores, samples with similar analytecompositions (i.e. scores) will be closer than those withdissimilar compositions. The similarities among samplescan shed light about the contamination sources.Details of this method are provided by Einax et al.

(1997). In this study, the PCA was performed usingStatistica 5.4 for Windows (Statsoft Inc., 1997). ThePAH concentrations were log-transformed to show abetter agreement with a normal distribution, as requiredfor PCA. Furthermore, a varimax normalised rotationwas applied to the obtained principal components. Thenumber of principal components to be retained wasdetermined by the Scree Test criterion (Cattell, 1966).

4. Results

The descriptive statistics of the PAHs concentrationsin CIP and CARB soil are presented in Table 1. The

PAH concentration distribution shows extremely highskewness and kurtosis values. After the data log-trans-formation, the consistent reduction of skewness andkurtosis indicates a better agreement with a normal dis-tribution (the probability plot before and after the log-transformation are not presented but reported as sup-plemental material and available on request).

4.1. Areal distribution of PAHs in CIP and CARB sites

Variography and gridding were applied to the totalPAHs measured in both CIP and CARB soils. Anexplorative investigation by considering the highestconcentration measured in the unsaturated zone (0±4 mdepth) for each sampling station is presented here. Thechoice of the highest value was suggested by the factthat contaminants in unsaturated zone accumulated inpermeable soil lenses irregularly distributed at di�erentdepths. Moreover, the interpolation obtained by con-sidering di�erent soil layers (0±1.5; 1.5±3; 3±6 m depth)showed a remarkable reduction of interpolating pointsin the east area, making the interpolation less explica-tive of the general trend of the contamination. However,the contour maps were in agreement with the con-siderations below relative to the entire unsaturatedzone.Directional variograms (east±west and north±south

directions) showed no marked anisotropy, therefore anomnidirectional variogram was selected and modelledwith a spherical variogram (Fig. 2). The variogrammodel was used as input to the kriging and the resultingcontour map is shown in Fig. 3. The contour map dis-plays decreasing total PAH concentration from CARBto CIP, with the exception of the high concentrationsmeasured in the settling tank area. It strongly suggests amigration of PAHs from east to west (i.e. from CARB

Table 1

Descriptive statistics of polycyclic aromatic hydrocarbon (PAH) concentrations (0±7 m depth)

Raw data (mg/kg dry wt) Log-transformed data

Mean S.D. Det.L. Max. Skew. Kurt. Skew. Kurt.

Acenaphthylene 47 124 0.01 825 4 23 0.6 1.4

Anthracene 17 60 0.01 382 5 28 0.7 1.4

Benzo(a)anthracene 4 15 0.01 89 5 27 0.7 1.4

Benzo(b)¯uoranthene +benzo(k)¯oranthene 17 65 0.01 438 6 32 0.7 1.5

Benzo(a)pyrene 2 5 0.01 37 5 30 0.7 1.5

Chrysene 5 16 0.01 91 5 23 0.7 1.4

Fluoranthene 19 78 0.01 649 7 58 0.9 1.8

Fluorene 25 65 0.01 356 4 16 0.6 1.2

Indeno(1,2,3-cd)pyrene 6 18 0.01 131 5 30 0.7 1.5

2-methylnaphthalene 168 360 0.01 1693 3 8 0.5 0.9

Naphthalene 374 968 0.01 7297 5 35 0.7 1.5

Phenanthrene 50 146 0.01 959 5 23 0.7 1.4

Pyrene 15 56 0.01 436 6 44 0.8 1.6

Benzo(ghi)perylene 7 16 0.01 82 4 14 0.6 1.1

Total PAHs 755 1626 0.01 10 888 4 20 0.6 1.3


area to CIP area). It can be noted that the in¯uence ofthe highest measured concentrations is smoothed bytaking into account a large nugget e�ect; in fact, themaximum concentration class in the map is 3000±4000mg/kg dry wt, whereas the total PAHs maximum con-centration in the unsaturated zone is 6267 mg/kg dry wt.

4.2. Vertical distribution of PAHs in CIP and CARBsoils

Fig. 4 shows the average PAH composition withinthree di�erent depths zones (0±1.5 m, 1.5±2.5 m and2.5±5 m depth) in west CIP soil (samples 1±5, E3±E13and Car16) and Fig. 5 in east CIP and CARB soils(samples E14, Car3±Car14). PAHs are indicated on thex-axis according to the decreasing solubility order, while

the average percentage of each PAH is reported on they-axis.From Fig. 4 it can be noted that in the CIP area the

relative percentage of more soluble compounds (left sideof the x-axis), such as methylnaphthalene, acenaphthy-lene, ¯uorene and phenanthrene, is low in both the sur-face (0±1.5 m) and subsurface (>1.5 m deep) soil, whilenaphthalene increases from 10% near the surface to40% in the underlying layers.

Fig. 2. Estimated variogram from logarithms of total polycyclic aro-

matic hydrocarbon (PAH) concentrations in soil (0±4 m depth).

Fig. 3. Contour map of the distribution of total polycyclic aromatic hydrocarbons (PAHs) concentration in CIP and west CARB soil (0±4m).

Fig. 4. Average polycyclic aromatic hydrocarbon (PAH) composition

in CIP soil at di�erent depths. Np, naphthalene; Mnp, 2-methyl-

naphthalene Ace, acenaphthylene; Fn, ¯uorene; Ph, phenanthrene; Ft,

¯uoranthene; Py, pyrene; Ant, anthracene; B(a)a, benzo(a)anthracene;

B(b)f, benzo(b)¯uoranthene; B(k)f, benzo(k)¯uoranthene; Chr, chry-

sene; B(a)p, benzo(a)pyrene; B(ghi)p, benzo(ghi)perylene; I(1,2,3-cd)p,

indeno(1,2,3-cd)pyrene.


On the contrary, the percentage of less soluble com-pounds (right side of the x-axis), especially ¯uor-anthene, pyrene, anthracene, chrysene, benzo(a)pyreneand indeno(1,2,3-c,d)pyrene, is around 10% in the sur-face soil and decreases to around 5% in the underlyinglayers. The only exception is benzo(a)anthracene, whichincreases from 10 to 30% in the deeper layer (2.5±5 m). Incontrast, in the CARB area (Fig.5) the percentage com-position of more soluble compounds, mainly naphthaleneand methylnaphthalene, is very high along the entiredepth pro®le, and the relative percentage of less soluble

compounds is very low everywhere. The vertical distribu-tion of benzo(a)pyrene, representative of less solublePAHs (water solubility: ca. 0.05 mg/l), and naphthalene,representative of more soluble PAHs (water solubility:ca. 31700 mg/l) have been investigated. Due to the irre-gular distribution of sampling points, it was impossibleto select a su�cient number of sampling points along atransect across the area for representing a soil section.Thus, all the sampling points within a east±west bandca. 40 m width (Fig. 3) were accounted for the distancefrom the western CIP border and plotted on the dia-gram of distance versus depth, as if they were `projected'on an imaginary west±east soil section. Directional var-iograms of benzo(a)pyrene and naphthalene log-con-centrations were calculated along the horizontaldirection and the depth (Fig. 6). Both benzo(a)pyreneand naphthalene showed a range `of in¯uence' along thehorizontal direction longer than along the depth pro®le.It may indicate a preferential movement of contaminantin the horizontal direction, due to presence of irregu-larly distributed impermeable lenses in the unsaturatedzone (ANPA et al., 1998) as well as contaminant trans-port in the phreatic aquifer. The range along the hor-izontal direction for naphthalene was three times longerthan for benzo(a)pyrene. It indicates the higher mobilityof naphthalene, as expected on the basis of its solubility.The resulting Kriged contour plots of benzo(a)pyreneand naphthalene are presented in Figs. 7 and 8, respec-tively. These contour plots can be used as exploratory

Fig. 5. Average polycyclic aromatic hydrocarbon (PAH) composition

in CARB soil at di�erent depths. See Fig. 4 for de®nition of abbre-

viations.

Fig. 6. Estimated directional variograms from benzo(a)pyrene and naphthalene log-concentrations within the east-west band plotted versus depth.


tools to evaluate the vertical distribution of PAHs in theCIP and CARB soils in the west-east direction.Fig.7 shows heavy benzo(a)pyrene contamination in

the surface soil at the west side of the CIP area, andonly an hotspot in CARB soil from 2 to 4 m depth. Thispro®le strongly suggests benzo(a)pyrene discharges onCIP surface soil, especially in the settling tanks.On the contrary, Fig. 8 shows heavy naphthalene

contamination in the CARB area along the entire depthpro®le, with a shape extending toward the CIP area inlower layers.

4.3. Statistical multivariate analysis

The PAH composition of the aromatic hydrocarbonblends produced by the CARB factory (Ambert, 1995)are reported in Table 2.

In order to investigate the origin of the CIP andCARB soil contamination, PCA was applied to the datamatrix composed of PAH composition (%) in the CIPsoil (46 samples, 0±7 m depth), and the CARB soil (25samples, 0±7 m depth), as well as of CARB factoryproducts (13 samples).The ®rst two factors, explaining 62% of the total

determinable variance, were retained. The correlationbetween the variables and the factors, the so-called fac-tor loadings, are reported in Table 3 where the PAHsare listed in order of increasing solubility. Factor 1 ispositively correlated to ¯uoranthene, pyrene, chryseneand benzo(a)pyrene (i.e. less soluble PAHs) and nega-tively correlated to naphthalene and methylnaphthalene(i.e. more soluble PAHs). Factor 2 is negatively corre-lated to ¯uorene and phenanthrene (i.e. moderatelysoluble PAHs).

Fig. 7. Contour plot of the benzo(a)pyrene concentrations within the

east±west band plotted versus depth.

Fig. 8. Contour plot of the naphthalene concentrations within the

east±west band plotted versus depth.

Table 2

Percentage composition of polycyclic aromatic hydrocarbon (PAH)a blends producted by the CARB factory

Cat 53 O.MG O.C NFT O.LG O.MP S970 O.ANT MS1 MS5 O.NER C.SOL O.MIS

Np 53.8 61.0 100.0 99.9 0.8 0.0 52.7 6.4 97.7 95.8 15.0 100.0 24.3

Mnp 9.9 11.4 0.0 0.1 67.9 0.6 44.6 3.7 2.3 4.2 2.7 0.0 22.9

Ace 1.5 5.2 0.0 0.0 20.4 5.9 0.0 6.7 0.0 0.0 8.0 0.0 8.8

Fn 6.2 5.6 0.0 0.0 9.2 14.1 1.5 7.9 0.0 0.0 12.4 0.0 6.7

Ph 14.0 9.3 0.0 0.0 1.4 42.3 1.0 26.6 0.0 0.0 31.7 0.0 16.5

Ant 2.9 2.6 0.0 0.0 0.3 12.0 0.1 9.0 0.0 0.0 8.8 0.0 2.6

Ft 5.6 2.9 0.0 0.0 0.0 13.1 0.0 16.8 0.0 0.0 11.1 0.0 8.7

Py 3.8 2.0 0.0 0.0 0.0 9.5 0.0 14.1 0.0 0.0 8.0 0.0 6.9

Chr 1.1 0.0 0.0 0.0 0.0 2.0 0.0 6.4 0.0 0.0 1.7 0.0 1.4

B(a)a 0.7 0.0 0.0 0.0 0.0 0.3 0.0 1.5 0.0 0.0 0.3 0.0 0.6

B(a)p 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.0 0.3 0.0 0.6

a Np, aphthalene; Mnp, methylnaphthalene; Ace, acenaphtylene; Fn, ¯uorene; Ph, phenanthrene; Ant, anthracene; Ft, ¯uoranthene; Py, pyrene;

Chr, chrysene; B(a)a, benzo(a)anthracene; B(a)p, benzo(a)pyrene.


The scores of factor 2 versus scores of factor 1 scat-terplots are shown in Fig. 9. The factor scores of sam-ples taken at the same sampling station at di�erentdepths were averaged, in order to obtain more mean-ingful plots. The close distances in the factor plotsindicate a signi®cant correlation between the CARBsamples and the CARB products, in particular for theproducts S970, O.C., MS1-5, C.Sol., NFT, all char-acterised by high concentrations of naphthalene. Thesoil samples from the CIP area show higher values ofthe factor 1, i.e. higher percentage of less soluble PAHs,than CARB soil samples and CARB products.The di�erence between the PAH contamination in

CIP and in CARB was further investigated by applyingPCA to the PAH concentration matrix composed of 14

individual compounds for 71 soil samples, of which 46were collected in the CIP area, and 25 in the CARB area,0±7 m depth. The ®rst two principal components,explaining 80% of the total determinable variance, wereretained. In Table 4, the relative factor loadings, solubility(S), organic carbon partition coe�cient (Koc) and Henry'sconstant (H) are presented (from Mackay et al., 1992) foreach PAH listed in order of increasing solubility.It can be noted that each factor is signi®cantly corre-

lated with a di�erent group of compounds. The mostsoluble and volatile, but least adsorptive PAHs (naph-thalene, 2-methylnaphthalene, acenaphthylene, ¯uoreneand phenanthrene) are correlated to factor 1, whereasthe less soluble and volatile but more adsorptive PAHsare correlated with factor 2. The exceptions are ben-zo(b)¯uoranthene+benzo(k)¯uoranthene and benzo(-g,h,i)perylene, that show a low solubility but are weaklycorrelated with factor 1.The scores of factor 2 versus scores of factor 1 scat-

terplot is presented in Fig. 10. The sharp di�erentiationof CIP and CARB samples into two clusters is mainlymade by factor 1, since CARB sampling stations arecharacterised by high factor 1 values (i.e. higher contentof soluble, volatile and not very adsorptive PAHs).

5. Discussion

PAH distribution in CIP soil is remarkably di�erentfrom those of Pb and Et4Pb. Whereas Pb and Et4Pbcontamination a�ects only the west CIP area and ismainly con®ned to the surface soil, PAH contaminationalso a�ects the east CIP area and the deeper soil layers.This is mainly due to di�erent pollution sources. As withPb and Et4Pb, the PAHs have been directly discharged

Table 3

Loadings of the ®rst and second factors of polycyclic aromatic

hydrocarbon (PAH) composition in CIP soil, CARB soil and CARB

productsa

Compounds Loadings

Factor 1 Factor 2

Naphthalene ÿ0.78 0.38

2-Methylnaphthalene ÿ0.69 ÿ0.12Acenaphtylene ÿ0.46 ÿ0.62Fluorene ÿ0.23 ÿ0.81Phenanthrene 0.38 ÿ0.80Fluoranthene 0.75 ÿ0.15Pyrene 0.85 0.07

Anthracene 0.53 ÿ0.48Benzo(a)anthracene 0.26 0.42

Chrysene 0.77 0.07

Benzo(a)pyrene 0.79 0.27

% explained variance 40 22

a Factor loadings > 0.7 are shown with bold numbers.

Fig. 9. Factor score distribution for polycyclic aromatic hydrocarbon (PAH) percentage composition in CIP (*) and CARB (*) soil samples, and

in CARB factory products (*). The more characterising PAHs for factor 1 and factor 2 are reported along the relative axis scale.


into the settling tanks and on CIP soil surface, as inferredfrom the presence of hot spots in the unsaturated zoneoutlined in the contour plot (Fig. 3). However thedecrease in concentration from east to west (Fig. 3), i.e.from the heavily PAH-contaminated CARB area to CIParea, and the naphthalene vertical concentration pro®le(Fig. 5), suggests a migration of more soluble PAHsthrough the phreatic and semicon®ned aquifer fromCARB to CIP. Due to the low porosity of the phreaticaquifer (sandy-silt levels perched on clayey intercala-tions), the PAHs migrated mainly through the semi-

con®ned aquifer (gravelly layer), as shown by the PAHmeasures in phreatic and semicon®ned groundwater(Carlon et al., 2000). PAH migration from east to west isnot in agreement with the current phreatic and semi-con®ned groundwater ¯ow directions toward the north-east. However, it can be expected that pumping from sur-rounding wells until the 1980s produced local disturbancesof the phreatic and semicon®ned ¯ow, which subsequentlyhas changed the local groundwater ¯ow regime.On the basis of the PCA analysis, it can be reasonably

inferred that the PAHblend discharged onCIP andCARB

Table 4

Loadings of the ®rst and second factors of polycyclic aromatic hydrocarbon (PAH) concentrations in soila

Compounds Loadings S (mg/l) log Koc H

Factor 1 Factor 2 log l/kg (atm m3/mol)

Naphthalene 0.94 ÿ0.07 31 700 3.11 1.18E-3

2-Methilnaphtalene 0.96 ÿ0.08 27 000 3.40 1.18E-3

Acenaphthyene 0.89 0.17 3930 4.00 1.14E-4

Fluorene 0.94 0.09 1980 3.86 1.17E-4

Phenanthrene 0.87 0.40 1290 4.15 6.05E-3

Fluoranthene 0.66 0.64 260 4.58 6.70E-2

Pyrene 0.59 0.72 135 4.58 7.00E-9

Anthracene 0.72 0.58 73 4.15 6.75E-2

Benzo(a)anthracene ÿ0.13 0.84 9.4 6.14 1.38E-8

Chrysene 0.19 0.93 1.6 5.30 1.18E-8

Benzo(b)¯uoranthene+benzo(k)¯uoranthene 0.75 0.40 1.2 5.74 2.01E-5

1.07E-8

Benzo(a)pyrene 0.00 0.91 50 E-3 5.59 1.39E-9

Benzo(ghi)perylene 0.71 0.44 0.7 E-3 6.20 1.4E-7

Indeno(1,2,3-cd)pyrene 0.49 0.39 0.5 E-3 7.53 5.07E-12

% Explained variance 51 29

a Factor loading > 0.8 are highlighted with bold numbers. The PAH solubility values (S), organic carbon/water partition coe�cients (Koc) and

Henry constants (values from Mackay et al., 1992) are reported in the last third columns.

Fig. 10. Plot of scores of factor 2 versus scores of factor 1 for the polycyclic aromatic hydrocarbon (PAH) concentration in CIP and CARB soil

samples.


soil was similar to the CARB factory products. In fact, theplot of PCA scores in Fig. 9 shows that PAH contamina-tion of CARB soil are correlated to the CARB factoryproducts. The minor content of more soluble, volatile andless adsorptive PAHs in CIP soil in comparison withCARB soil, outlined by the PCA scores in Figs. 9 and 10,and the fractionation down the soil pro®le of PAHsaccording to their solubility properties (Figs. 4 and 5), canbe easily explained by considering weathering processesoccurring in CIP soil. These include the depletion in thesurface soil layer of more volatile PAHs by evaporation,the leaching of more soluble PAHs down the soil pro®le byrain water percolation and phreatic groundwater level¯uctuations, as well as PAH biodegradation. In fact, it iscommonly observed that biodegradation is more e�ectiveon lower molecular weight and soluble PAHs (Bosset andBartha, 1986; Park et al., 1990; Grosser et al., 1995).The fact that weathering processes in CARB soil are

less evident than in CIP soil is caused by the ongoingpollution of CARB soil until recent times.Furthermore, Fig. 10 shows that samples collected in

the settling tanks, labelled with number 2, are char-acterised by high concentration of more soluble PAHs,similar to CARB samples. This is not surprising, since inthe settling tanks the low permeability bottom couldreduce leaching and the extremely high concentration andtoxicity of Et4Pb might inhibit the microbic degradation.The vertical concentration pro®les of benzo(a)pyrene

and naphthalene (Figs. 4 and 5), as an example of theorigin and the behaviour of less and more solublePAHs, respectively, accord with soluble PAH migrationfrom CARB to CIP, PAHs discharges on CIP surfacesoil and weathering processes. In fact, the benzo(a)pyr-ene is retained in the unsaturated soil layer in the CIP

area, and is probably the unweathered residue of thePAH blend discharged on CIP soil. The naphthalene ismainly present in the CARB area, and a migration tothe CIP area is evident in the satured zone.To be used in risk assessment, the ®nal conceptual

model of the study area concerning fundamentalhydrogeological characteristics and the information sofar obtained on the contaminant distribution can bevisualised as in Fig. 11.

6. Conclusions

In this case study, additional information from thedata set, which is relatively small and a�ected by sam-pling gaps, was extracted by Kriging and PCA. It pro-vided fundamental results to risk assessment, such asthe determination of the primary and secondary pollu-tion sources, and allowed the development of a reliableconceptual model of the site. The study of the spatialcorrelation of the concentration data allows preliminaryinformation on the general pattern of contaminative pro-cesses and geophysical phenomena to be incorporated inthe interpolating exercise. Moreover, the variogram canbe judged on the basis of the expert knowledge of thesite. It makes Kriging a reliable technique for investi-gating the distribution and sources of contaminants insoil, even when relative small data sets and not exhaus-tive samplings are available. However, variograms ofpollutants in soil often show a large nugget, due to sam-pling-analytical errors and small-scale variability. Itshould be remembered that including a nugget in krigingsmoothes the highest concentrations. It outlines the gen-

Fig.11. Conceptual model of the hydrogeology and the contamination of the site. PAH, polycyclic aromatic hydrocarbon.


eral trend of the contamination, but in case of few data itwould mask the presence of hot spots and singularities.PCA is a very promising tool for risk assessment-

oriented characterisation studies. The PCA interpreta-tion according to the physico-chemical properties ofcontaminants provides a new insight in the contaminantenvironmental behaviour and sources. In the case ofPAHs simple source scenarios, the PCA would be pre-ferred to other ®ngerprinting methods, such as thosebased on speci®c source and weathering ratios (Page etal., 1988; Sauer et al., 1993; Douglas et al., 1996), whichwould require the analysis of a large spectrum of alkylsubstituted and unsubstituted PAHs.In this case study, the extended investigation about

the primary sources of the contamination proved usefulboth for identifying pollution liabilities and even moreso for a better understanding of contaminant fate andtransport at the site (degradative processes, extent oftransport through the soil medium and the aquifer).Furthermore, the investigation of neighbouring pol-

lutant sources was necessary to understand the con-taminant distribution and characterise the externalsources of risk. Indeed, the risk-based characterisationof a site should encompass the collection of availabledata on the neighbouring areas.

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