Investigation of anthropic effects connected with metal ions concentration, organic matter and grain size in Bormida river sediments

Analytica Chimica Acta 560 (2006) 172–183

Investigation of anthropic effects connected with metal ionsconcentration, organic matter and grain

size in Bormida river sediments

Emilio Marengoa,∗, Maria Carla Gennaroa, Elisa Robottia, Piero Rossanigob,Caterina Rinaudoa, Manuela Roz-Gastaldia

a Department of Environmental and Life Sciences, University of Eastern Piedmont, Via Bellini 25/G, 15100 Alessandria, Italyb ARPA (Agenzia Regionale Protezione Ambientale) Piemonte, Spalto Marengo 35, 15100 Alessandria, Italy

Received 29 July 2005; received in revised form 24 November 2005; accepted 29 November 2005Available online 3 February 2006

Abstract

An investigation on the sediment composition and grain size was carried out along the Bormida river (Piedmont, Northern Italy). The samplesw of primaryp of pollution,o heavy metali on the banksw the hot spotr©

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ere taken both in the riverbed and on its banks. Multivariate statistical exploratory methods permitted to identify possible sourcesollution. In particular, principal component analysis (PCA) showed that there are probably at least three anthropogenic main causesne connected with Cr, Ni and Sn, the second with cadmium and the last one with mercury. Some correlations were found between

on concentrations and organic matter and/or the sediment grain size. Differences between the samples collected in the riverbed andere identified and the relationships between the principal components and the distance of the samples from the riverbed and from

epresented by the ACNA industrial site were also analyzed.2005 Elsevier B.V. All rights reserved.

eywords: Heavy metal pollution; Kohonen artificial neural networks; Principal component analysis; River sediments; Multivariate analysis

. Introduction

In the last decades, more and more attention has been devotedo the investigation of heavy metals pollution in the environment,ue to the increasing anthropogenic contribution to the naturalources of heavy metals in the environment.

Sediments have been studied worldwide as heavy metal pol-ution indicators in river systems: in Europe, the research groupsf Bertin and Bourg[1], Soares et al.[2] and Rubio et al.[3] gave

heir contribution to the study of heavy metals pollution applyingoth ICP-MS and atomic absorption techniques to the analysisf heavy metals in sediments from different aquatic basins. Moreecently, other research groups have performed similar studiesn other parts of the world: Huang and Lin[4] and Tsai et al.[5]n Taiwan, Simenov et al.[6] on the Black Sea.

The presence of heavy metals in sediment samples is dueo a variety of chemical, biological and physical effects: they

∗ Corresponding author. Tel.: +39 0131 360272; fax: +39 0131 360250.E-mail address: [email protected] (E. Marengo).

can be introduced as dissolved or particulate matter, whathey are due to natural processes (river flow fluctuations, wdrainage, runoff from river banks, leaching of bed rocks) or toanthropogenic contribution (industries discharging into rivurban wastes)[5,7]. The examination of the spatial differencethe heavy metals content of sediments allows the identificof the presence of anthropogenic effects, possible sourccontamination and effects due to the transport of pollutantsother areas[4].

Heavy metals in sediments can be present in different fothey can precipitate as hydroxides, sulphides, carbonatother minerals; be absorbed on manganese and iron oand hydroxides or co-precipitate with them; form organicinorganic complexes[1,8–10]. They are usually associatedorganic compounds in the so-called “thin fraction”[11]. Sed-iments can release pollutants according to the changes oring in the chemical–physical conditions of the environmvariations of pH, salinity, redox potential, presence of orgchelants, complexation reactions, diagenesis processes[2,5].

Environmental problems are often translated in large datcharacterized by complex relationships existing between

003-2670/$ – see front matter © 2005 Elsevier B.V. All rights reserved.oi:10.1016/j.aca.2005.11.086

E. Marengo et al. / Analytica Chimica Acta 560 (2006) 172–183 173

descriptors, the samples and the samples-descriptors together.Multivariate statistical methods like principal component anal-ysis (PCA) or other clustering techniques have been widelyapplied in the study of environmental problems. Some examplesare reported in the book from Einax et al.[11]. The applicationof multivariate statistical techniques is often mandatory in thetreatment of datasets of high complexity. Recently PCA[12,13]and other multivariate statistical techniques have been appliedto the analysis of heavy metals levels in sediments belongingto river systems for which heavy metals pollution effect wassuspected[2,3,6]. The investigation of heavy metals content inenvironmental samples is particularly suitable when monitoringthe contamination level of polluted areas (the so-called “hot-spots”) must be achieved.

The present article describes the investigation of the heavymetal content of sediments belonging to a hot spot in NorthernItaly, Piedmont region. The chosen area is the so-called AziendaColoranti Nazionali e Affini (ACNA) site, located in Cengio, atthe border between Piedmont and Liguria (in the Northwest ofItaly).

This factory was founded in 1882 as a small dynamite fac-tory (SIPE—Societa Italiana Prodotti Esplosivi). In 1931, it wasacquired by the Montecatini group and was converted into a dyefactory: it represented the first industrial plant of this type presentin Europe and it used to supply with coloring agents a wide vari-ety of industries (principally belonging to the trade of textiles,p sedb denth o-d utedbh

g theB rac-t mpos rki ationo seno enta preei d tot n fort et oo rates nt ov ainea gneda ientsop es int so hipsb jecP

eda ation

K-ANNs, also called self organising maps (SOMs), are proba-bly the artificial neural networks most similar to the real brain.They are based on a single layer of neurons, usually arranged ina square box, with the responses on the top of the box[18–23].Below each cell of the top layer there is a column of cells, onefor each descriptor: these columns contain the weights of thenetwork. The artificial network is characterized by the numberof cells on each edge of the top layer, whose optimal size isusually selected by trial and error. In the learning step each sam-ple is in turn presented to the network and its distance fromevery column of weights is calculated. The “winning” neuronis identified as the cell of the top layer whose distance fromthe considered sample is minimal. The weights of the winnerneuron are then modified so that the distance of the same sam-ple from the winning neuron shall be smaller at the successivelearning epoch. All neurons in the neighborhood of the winnerundergo a similar correction, which decreases with the distancefrom the winning neuron. The distance at which the correctionis applied usually decreases as the learning epochs go by. At thebeginning, every correction is applied to all neurons, while inthe last cycles only the winning neuron is corrected. In a sim-ilar way, at the beginning the learning rate, i.e. the amount ofcorrection introduced, is larger than in the last cycles. The finalresult is a map, the first layer, where the most similar samplesare in the same cell or close one to each other. The weights giveinsight into the reasons leading to the observed clusterizationo eforei f thew theirs

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aper and leather). In 1998, the ACNA was definitively cloy the Environmental Ministry in consequence of an acciappened in the industrial plant[14]. Due to the prolonged pruction, the ACNA site represents a hot spot and it is polly both organic and inorganic contaminants[15], among whicheavy metals are some of the most important ones.

In this paper, a series of 31 sediments collected alonormida river both in its riverbed and on its banks was cha

erized by mineralogical analyses and the heavy metals coition (see Section2 for more details). The final aim of this wo

s the identification of possible sources of human contaminf the sediments and whether this can be ascribed to the pref the ACNA site or not. To this purpose, principal componnalysis was applied to the dataset in order to detect thence of the suspected heavy metal polluting effect. PCA[16,17]

s in fact a statistical multivariate analysis technique devotehe elimination of redundancy and the dimension reductiohe investigation of large datasets. PCA provides a new srthogonal variables, the principal components (PCs), geneo that each PC accounts for the maximum possible amouariance contained in the original dataset. The PCs are obts linear combination of the original descriptors and are alilong the directions of covariance of the data. The coefficf the linear combinations are calledloadings or weights. Therojection of the objects along the PCs, i.e. their coordinat

he new reference system, are calledscores. The joint analysif loadings and scores allows the identification of relationsetween the variables, the objects and both variables and obCA can thus be applied to the dataset.Kohonen Artificial Neural Networks (K-ANNs) were appli

s well, to investigate the presence of samples clusteriz

-

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ts.

.

f the samples: the analysis of the first layer provides thernformation on the similarity of the samples; the analysis oeights provides instead information about the reason forimilarity.

. Experimental

.1. ICP-MS analysis

The analysis of metals has been carried out by mean internal method ARPA. 3.0 mL H2O2 30% are added t.0 g of sample in a closed Teflon container. Twenty minfter, 3.0 mL HNO3 65% and 9.0 mL HCl 37% are addeineralization then takes place in microwave oven for 35 m0 min at 250 W, 10 min at 450 W, 10 min at 600 W amin at 250 W. During mineralization, an internal standardded (1.0 mL Tb 1000.0 mg/L). The mineralisation solutiorought up to 50.0 mL and two internal standards are adh 100.0�g/L (rhodium nitrate) and Au 200.0�g/L (aurumhloride—needed for the determination of Hg). For ICP-nalysis, a X5 ThermoElemental (Winsford, UK) was uhe instrument calibration is carried out by means of certtandards.

.2. X-ray diffractometry

The samples were analyzed by X-ray diffractometry toinguish the mineralogical phases present and their rebundances, with particular regard to clay minerals, resible for interaction processes with the pollutants. Bothotal sample and the fraction below 63�m were analyze

174 E. Marengo et al. / Analytica Chimica Acta 560 (2006) 172–183

by X-ray diffractometry. A powder diffractometer Philips PW1710 was used for this purpose, equipped with an anti-cathodeCo lamp (K� = 1.7902A). Angles 2θ ranging from 3 to 35◦were considered. The clay fraction was analyzed on orientedsamples, thus allowing the registration of the peaks producedby reflections of basal crystallographic planes only. The ori-ented samples were prepared by mixing 30 mg of sampleabove 2�m and 5.0 mL of de-ionised water. The suspen-sion was picked up and laid on a microscope slide. X-rayspectra were registered on the raw sample and after the fol-lowing treatments: ethylene-glycol expansion; 1 h heating at550◦C.

2.3. Organic content determination

The method for the evaluation of the total organic content isbased on the determination of the sample weight loss by cal-cination. The method is contained in the “Regolamento per losmaltimento in agricoltura dei fanghi residuati da processi didepurazione delle acque reflue” promulgated by the Presidenzadella Giunta Regionale del Piemonte, by DPGR no. 4558—June23, 1986 (BUR no. 26—July 2, 1986).

2.4. “Methylene-Blue” Test

clayf s ther argeo thy-l rface( wingg thoseb sizec t-w I.c

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Fig. 1. Bormida river with sampling sites.

The following 18 analyses have been performed in order tocharacterize the samples:

- total organic material (later indicated to as SO TQ) andtotal organic material below 63�m (indicated hereafter asS O 63M);

- metal ions concentrations (Ni, Cr, Cu, Zn, As, Cd, Sn, Hg, Pbby ICP-MS);

- amount of sand, silt and clay (hereafter indicated as Sand%,Silt%, Clay% by densimeter and pipette method);

- mineralogical analysis by X-ray diffraction of the frac-tion below 63�m: Smectite and Vermiculite%, Kaolinite%,Illite and Muscovite% (hereafter indicated as SMEVE,KAO, ILL MU, respectively), Methylene Blue value(VB).

The complete dataset is then constituted by a matrix 31× 18(Table 1); minimum–maximum values and standard deviationare instead reported inTable 2.

Additional features taken into consideration were the dis-tance from the river (later indicated as DALVEO), the distancefrom the ACNA site (indicated hereafter as DISTSCA; negativevalues indicate the location of the sampling site upstream fromACNA, along the Bormida river) and the already mentioned dis-t

This test indicates qualitatively and quantitatively theractions present in geo-materials; in particular, it quantifieetention ability of soils, due to the unbalance of electric chf the sediment particles, by measuring the amount of Me

ene Blue necessary for completely covering the total suinternal and external) of the particles. The particles shoenerally a larger surface and a larger charge deficit areelonging to clay minerals, usually abundant in the grainlass of clays (dimensions below 3.9�m, according to the Wenorth’s classification or below 2�m, according to the A.G.lassification).

The principal mineralogical components of the clay fracre phyllosilicates (illite, kaolinite, smectite, etc.), differenble to adsorb water, cations or organic molecules, accord

heir different crystalline structure[24].The parameter provided by the Methylene-Blue Test is “Vb”,

xpressed as grams of Methylene Blue in 100 g of dry samamples richer in clay present largerVb values[25]. Methylenelue Test was performed according to the method report

he norm AFNOR-NFP 94-068 (1993).

.5. Dataset

The dataset is constituted by 31 samples of sedim0–20 cm depth) taken along the Bormida river (Fig. 1).

The study was performed to evaluate the polluting effeche large industrial site ACNA, now closed, located in Cenlose to the river spring.

Two types of samples have been collected, namely fromiverbed, characterized hereafter as S samples, and froanks, defined as AL samples.
inction of the samples as S or AL.

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Table 1Overall dataset

Sample DISTSCA D ALVEO %SAND %SILT %CLAY S O 63M S O TQ Vb Cr Ni Cu Zn As Cd Sn Hg Pb SMEVE ILL MU KAO Kohonen

AL21 −2695 104 64.5 28.5 2.7 7.6 8.1 1.38 176.47 142.16 22.06 83.33 35.29 0.48 10.29 0.25 32.35 32 25 9 CAL1 −2625 104 85.4 13.6 0.5 4.9 1.9 0.70 122.81 43.86 29.82 105.26 43.86 0.57 8.77 0.09 61.40 8 58 20 (1,6)S1 −2615 20 16.7 70.4 6.9 8.7 11.9 1.89 181.82 107.95 20.45 227.27 14.77 1.59 4.89 0.09 28.41 21 25 15 BAL2 −1633 55 70.2 27.8 0.3 5.2 2.3 0.79 62.50 28.13 16.15 78.13 32.81 0.32 3.49 0.09 43.23 8 48 16 (1,6)S5 617 13 2.0 86.5 4.5 11.3 12.1 1.88 260.87 119.57 42.39 228.26 13.04 0.26 3.15 0.09 29.35 32 19 7 BAL22 671 81 57.8 34.4 4.6 6.6 6.8 1.70 204.08 81.63 30.61 102.04 51.02 0.40 12.24 0.39 43.88 11 37 17 ES6 2089 22 62.9 28.5 3.0 9.0 5.0 1.42 170.45 107.95 25.00 90.91 16.48 0.57 5.06 0.36 39.77 7 49 14 (2,6)AL13 2622 60 6.8 84.9 3.3 7.5 11.4 0.89 235.29 58.82 43.14 112.75 58.82 0.40 11.27 0.12 58.82 0 24 11 (6,6)S13 2745 10 67.3 25.2 4.5 5.0 4.3 1.55 154.32 86.42 27.16 98.77 30.25 0.33 3.02 0.10 50.62 14 34 16 (1,5)AL24 3350 248 40.7 37.8 14.7 7.3 4.9 3.46 391.51 231.13 47.17 80.19 19.34 0.47 7.08 0.09 25.47 43 18 3 DAL7 4110 48 30.0 57.6 5.4 7.1 6.0 2.67 202.02 90.91 26.26 116.16 26.26 0.50 2.58 0.76 43.43 37 15 9 FS7 4123 13 10.6 80.4 3.0 8.1 8.7 1.80 227.27 116.88 33.12 155.84 20.13 0.55 3.38 0.33 35.71 28 21 10 BAL15 7906 22 83.6 13.4 1.5 5.5 4.2 1.68 239.58 130.21 40.10 88.54 30.73 1.15 8.33 0.21 45.31 31 22 10 CAL9 14127 59 52.0 36.8 6.2 7.3 5.9 2.39 313.83 143.62 44.15 132.98 35.64 0.40 10.11 0.09 33.51 27 36 6 DS9 14161 10 67.6 28.4 2.3 6.8 5.7 1.29 291.67 154.76 55.36 119.05 23.21 2.92 10.12 0.34 77.38 27 30 10 (1,2)AL16 16936 32 50.9 39.1 4.6 4.8 4.2 1.65 187.50 100.96 19.71 48.08 35.10 0.40 10.58 0.25 14.90 19 28 9 CS16 17046 8 60.4 29.2 5.5 12.0 4.3 1.63 371.95 195.12 60.98 115.85 29.27 0.40 14.63 0.45 35.98 32 22 7 (1,1)AL26 20778 24 34.9 45.5 10.3 8.8 5.8 1.68 234.69 142.86 76.53 86.73 25.51 0.87 7.14 0.40 35.20 23 39 11 (1,1)S10 23822 6 26.4 61.2 6.9 7.2 5.7 1.61 275.00 175.00 48.00 110.00 14.50 1.20 6.50 0.17 21.00 38 21 6 BAL10 24689 140 57.2 32.3 6.9 5.3 4.7 1.70 211.54 115.38 24.52 57.69 28.85 0.47 9.13 0.96 20.19 30 30 10 EAL17 29645 70 56.7 31.2 6.4 6.0 5.1 1.74 138.61 79.21 22.28 217.82 16.34 0.34 1.88 0.50 28.71 27 33 4 (6,1)AL18 35335 65 31.5 51.0 10.0 5.9 6.4 2.40 202.25 151.69 29.78 84.27 20.79 0.73 3.37 0.50 32.02 28 26 11 FS11 41041 12 76.0 19.0 2.8 7.3 2.5 1.61 306.12 163.27 56.12 122.45 21.94 1.63 7.14 0.19 25.00 29 28 14 (1,2)AL11 41753 60 77.0 19.0 2.5 4.4 3.6 1.79 250.00 142.86 26.79 50.00 19.29 0.10 6.43 0.12 15.36 30 27 9 CAL28 46417 38 37.4 56.1 3.5 5.7 8.3 1.57 260.20 137.76 38.78 76.53 31.63 0.40 9.18 0.14 27.55 20 37 14 EAL19 49772 45 61.7 29.3 5.1 7.0 6.4 1.50 314.66 146.55 34.91 103.45 21.55 0.40 8.19 0.14 19.83 34 24 12 DAL29 53705 58 51.9 39.6 5.1 7.1 5.9 1.80 294.35 165.32 44.35 68.55 20.16 0.40 7.26 0.10 18.95 30 24 14 DS20 56953 5 32.3 52.7 8.1 8.4 8.2 1.69 170.45 113.64 30.68 204.55 8.98 0.73 1.70 0.09 17.05 37 19 4 BAL20 57066 32 63.2 32.5 2.5 6.8 3.6 1.48 234.69 127.55 27.55 51.02 19.90 0.40 6.63 0.09 18.37 29 24 15 CAL30 62464 53 56.0 34.7 5.3 4.6 2.6 1.91 290.82 132.65 22.45 50.00 22.45 0.40 7.14 0.09 14.29 33 23 14 DS12 67591 16 7.5 72.5 10.0 6.4 6.8 1.70 200.00 155.56 20.00 188.89 11.11 0.44 1.22 0.09 13.33 31 21 8 B

The last column contains the indication of the position of each sample on the top layer of the Kohonen Neural Network: letters indicate the group (Fig. 5), while the numbers indicate the (x, y) position on the toplayer.


Table 2Mean value and standard deviation of each measured descriptor

Variable Mean Dev Std.

%SAND 48.36 23.41%SILT 41.91 20.46%CLAY 5.13 3.13S O 63M 6.95 1.79S O TQ 5.91 2.63VB 1.71 0.52CR 231.53 71.52NI 125.46 42.72CU 35.04 14.13ZN 111.46 52.41AS 25.77 11.29CD 0.65 0.55SN 6.84 3.40HG 0.25 0.21PB 32.46 15.30SME VE 25.68 10.32ILL MU 28.61 9.84KAO 10.81 4.15

2.6. Software

PCA and the graphical representations were performed byStatistica v.6.1 (Tulsa, OK, USA) and Excel 2000 (MicrosoftCorporation, USA). Kohonen self-organising-maps were calcu-lated by a self-developed software in Visual Basic.

3. Results and discussion

The investigation focused here on shedding light on the rela-tionships between the variables and (1) the possible differencesexisting between S and AL samples, (2) the geographic variablesand (3) systematic profiles indicating potential anthropogeniceffects. For an exhaustive analysis of the correlation matrix cal-culated on the overall dataset, see the paper by Rossanigo et al.[26]. PCA and K-ANNs were applied here to further investigatethe relationships between samples and variables. In particulathe differences between S and AL samples were considered herrather than the relationships between variables involving all thesamples contemporarily.

3.1. Principal component analysis

The correlation structure of the dataset appears quite complex, so a PCA was performed in order to simplify its anal-y blesw unh

d inT ticalls po-n asb sitivo otet po-n

Table 3Variance explained by the first five PCs

PC Explainedvariance (%)

Cumulativevariance (%)

1 31.99 31.992 18.42 50.423 13.68 64.104 8.24 72.345 6.79 79.13

%Sand and As show large negative loadings, while SMEVE,Clay%, VB and Ni show large positive loadings. As commonlyacknowledged, VB is larger for clayish fractions[25], in factit is positively related to Clay%. The first PC explains thus thedifferences in the adsorbing ability of the different fractions:Illite–Muscovite, Kaolinite and Sand show a smaller adsorptionwith respect to Smectite–Vermiculite and Clay. PC2 presentslarge positive loadings for Sand, Cr and Ni and large negativeones for Silt, SO TQ and Zn. This PC suggests that Zn is mainlyrelated to the silt fraction, while Cr and Ni to the sand fraction.PC2 also points out that Cr and Ni are positively correlated,while they are negatively correlated to Zn.

The third principal component shows large positive loadingsfor Cu, Sn, Pb and SO 63M, thus mainly accounting for theinformation about Cu and Pb pollution. PC4 explains the oppo-site behavior of As (positive loading) and Cd (negative loading),pointing out the negative correlation of these two variables inthe dataset (when Cd increases, As decreases and vice versa).Finally, the fifth PC accounts for the information about Hg pol-lution.

Some of the relationships among the variables, pointed out byPCA, are well known in sediment samples; however, the inter-relationships found are useful for deepening the features of thecollected samples with respect to their distance from the riverflow and the ACNA site. For this purpose, it is interesting to rep-resent the dependencies of the first five principal components ont urp m,utt i-m ots nor sincet howsa t ther romA ndert ples(

sp theirl sam-p atedu e tot ite.T ever,

sis by eliminating the variables correlation. The variaere autoscaled before PCA, to eliminate their scaleomogeneities.

The variance explained by the first five PCs is reporteable 3. The successive PCs were not considered as statisignificant. The loading plots of the first five principal coments are represented inFig. 2. The loadings are representedar diagrams: dark bars indicate variables showing large por negative loadings on the corresponding PC. It can be n

hat the variables weighting more on the first principal coment are related to the mineralogical fractions: ILLMU, KAO,

r,e,

-

-

y

ed

he geographic variables (Fig. 3). Fig. 3 represents the contolots of the five significant PCs built with the spline algorithsing the geographic variables asx- andy-values (distance from

he ACNA site (x-axis); distance from the riverbed (y-axis)) andhe values of the scores of each PC asz-values; each experental sample is represented as a white circle. In these pl

eliable considerations can be driven for the top right area,here are no samples collected in this region. Each plot sn horizontal and a vertical black line: samples located aight from the vertical black line were collected upstream fCNA; samples present at the bottom of the scatter plots (u

he horizontal black line) represent instead the riverbed samS samples).

According to the contour plot of PC1 (Fig. 3a), S sampleresent a larger adsorption ability (positive scores) due to

arger abundance in Clay and Smectite–Vermiculite; theseles show a larger content of Ni as well. AL samples locpstream from ACNA show a smaller ability to adsorb du

heir larger abundance in Silt, Illite–Muscovite and Kaolinhe adsorption ability of AL samples seem to increase how


Fig. 2. Loadings plots (bar diagrams) of the first five principal components.

by increasing their distance from ACNA (samples along the hor-izontal black line). At a small distance from ACNA, increasingthe distance from the river flow decreases the sample adsorptionability: the only exception is the sample located at the largestdistance from the river flow.

The contour plot of PC2 (Fig. 3b) allows the investigationof the behavior of the samples with respect to their content ofCr, Ni and Zn. Samples located near the ACNA site (upstreamand both in the riverbed and on its banks) are characterized bya low content of Cr and Ni and a larger amount of Zn (negativescores). For what regards the S samples, instead, they presenta large content of Cr and Ni at a distance from the ACNA siteranging from 12,000 to 45,000 m. AL samples instead showlarge amounts of Cr and Ni even at a larger distance from theACNA site (45,000–65,000 m), pointing out some differenceexisting between S and AL samples. At small distances from

ACNA (along the vertical black line in the graphic), the contentof Cr and Ni seems to increase with the increasing distance fromthe riverbed. The second PC thus evidences the presence of apolluting effect of Cr and Ni, which can be due to two differentcauses: the first one being the presence of the ACNA site (Crand Ni content is low upstream from ACNA, while it increasesdownstream); in this case however, there seems to be in additiona river flow effect, since Cr and Ni pollution is not present in exactcorrespondence with the ACNA site but at a distance rangingfrom 10,000 to 45,000 m. The other cause can be the presence ofanother source of Cr and Ni pollution, located downstream fromACNA. Furthermore, Cr and Ni part differently in the riverbedand on its banks, as stressed by the different behavior of S andAL samples at different distances from ACNA.

The third PC accounts for another source of pollution, namelythe presence of Cu, Sn and Pb in the environment, in fact these


Fig. 3. Contour plots built with the spline algorithm, representing the dependency of PC1 (a), PC2 (b), PC3 (c), PC4 (d) and PC5 (e) on the geographic variables.Colors from light to dark red correspond to the increasing positive score, while colors from light to dark blue correspond to the increasing negative score.

components are present at low concentration in the sampleslocated before (upstream) ACNA. For what regards the S sam-ples, the pollution due to these metals is present from about10,000 to 42,000 m from ACNA and it decreases at larger dis-tances. The AL samples show instead a different behavior, infact the largest amount of Cu, Sn and Pb is recorded at smalldistances from ACNA. Therefore, Cu–Sn–Pb pollution seems tobe transported downstream by the river flow (pollution present

at larger distances from ACNA in S samples), while it is quitelocalized near the ACNA site for what regards bank samples.Moreover, if a small distance from ACNA is taken into account,Cu–Sn–Pb pollution seems to be located at a distance from theriverbed ranging from 20 to 140 m, while it decreases at largerdistances from the riverbed. For this PC too, S and AL samplespresent a different behavior due to their different mineralogicalnature.


The contour plot of PC4 (Fig. 3d) accounts for the informationabout As (positive loadings–positive scores) and Cd (negativeloadings–negative scores). The behavior of the samples withrespect to the As and Cd content is mainly responsible for thedifferences existing between the two groups of samples: S sam-ples are in fact characterized by a larger content of Cd, while ALsamples are richer in As. Cd and As pollution however, does notseem to be ascribable to the presence of the ACNA site, sincethey are present in S and AL samples, respectively, even in thesamples collected upstream from ACNA. The fourth PC seemsthus to account for a source of pollution which is not directlyrelated to the presence of the ACNA. Another consideration thatcan be driven about the different behavior of Cd and As in sed-iments regards their possible different speciation: Cd probablyforms more soluble species (more abundant in the riverbed),while As forms preferentially less soluble ones (richer in ALsamples).

PC5 (Fig. 3e) mainly accounts for the information about Hg,which represent another potential source of pollution. As forAs–Cd, even in this case the source seems not to be relatedto the presence of the ACNA site: Hg in fact mainly parts inAL samples and it is localized downstream from ACNA at adistance ranging from about 20,000 to 30,000 m. The content ofHg is another cause of difference between S and AL samples:Hg shows the largest content in AL samples and only a smallamount can be recorded in S samples, immediately downstreamf

stlyd sseso portedi ing tot s tol pedaa ion:t low.T e andv nd al

Fig. 4. Scatter diagram of PC1 and PC4, which shows a good separation betweenthe two classes of samples (S and AL).

The results driven by means of PCA are in good agreementwith those driven on the basis of the analysis of the correlationmatrix [26].

3.2. Kohonen self-organizing maps

Kohonen self-organizing maps were applied to further ana-lyze the samples clusterization and the physico-chemical param-eters related to the clusterization. These maps allow to recog-nize local similarity which can be particularly useful in theanalysis of environmental samples where different reasons ofsimilarity may contemporarily exist. Several architectures ofthe network were investigated but the one providing the bestresults was a 6× 6 top layer. A total of 500 iterations wereused, with the learning rate decreasing linearly from 0.5 to0.01. In the same time the range of the weights correctiondecreased from 6 to 1. The variables were range scaled before theanalysis.

The representation of the first layer was split in two parts(Fig. 5), one for each group of samples (S (Fig. 5a) and AL(Fig. 5b)) and it represents the number of samples assigned toeach cell of the top layer: each bar thus represents the number

6 ma

rom ACNA.PC1 and PC4 seem to be the two principal components mo

iscriminating between S and AL samples in fact the two claf samples appear well separated in the scatter diagram re

n Fig. 4: S and AL samples appear thus separated accordheir different mineralogical nature, causing the two groupink different pollutants. In particular, AL samples are grout positive values on PC1 and negative values on PC4, so theyre richer in kaolinite, illite and muscovite and sand fract

hey show thus a larger content in As, while Ni and Cd arehe S samples, instead, are richer in silt, clay and smectitermiculite, so they show a larger content in Ni and Cd aow concentration of As.

Fig. 5. First layer of the Kohonen 6×
p: S samples (a) and AL samples (b).


Fig. 6. Surface plots of the scores of the first five principal components and the two geographic variables built with the spline algorithm using the samples co-ordinateson the first layer asx- andy-values and the values of each descriptor asz-values: scores PC1 (a), scores PC2 (b), scores PC3 (c), scores PC4 (d), scores PC5 (e),D SCA (f), D ALVEO (g).


of object in the corresponding position of the top layer. As justpointed out in the theory section, similar objects are present inthe same position or form groups on the top layer.Fig. 5pointsout (group A) that the only cell containing both an AL and anS sample is that in position (1,1), which contains samples S16and AL26; the other cells contain only S or AL samples.

From Fig. 5, it is possible to identify some groups: S sam-ples (Fig. 5a) present only a group of 6 samples (group B) atx = 5–6 andy = 2–5; these samples however do not represent anhomogeneous group, since two AL samples are present at theextreme of the group (y = 1 andy = 2; Fig. 5b). The AL samplespresent instead three groups: group D (4 samples) is clearly sep-arated from the others atx = 3–4 andy = 1–2. Groups C and Eappear instead less clearly separated since they are surroundedby S samples. Finally, two stand-alone samples (AL7, AL18)are present in positionx = 4, y = 4 (group F).

Insight on the differences existing among the identifiedgroups of objects can be obtained by investigating the behav-ior of the significant principal components and the geographicvariables in thex–y top layer.

Fig. 5represents the surface plots of the five significant PCsbuilt with the spline algorithm, using the samples co-ordinateson the first layer asx- andy-values and the values of the scoresof each PC asz-values (Fig. 6a–e). The last two representations(Fig. 6f–g) represent instead the samples co-ordinates on thefirst layer (x- andy-values) and the values of the distances fromA

samp thisw faco wellfi , alp gfuli

l (1,6( ition( fors fore,t unt oS reamf 0 m( inp ps Bat re al tantf atedi wst p Bw lla mple( NA( eamga avioi me-

diate distances from ACNA. Group E and D are far from theriverbed (D is the most distant one), while group C and samplesin (1,1) are riverbed samples (red areas inFig. 3b). The secondPC indicates that the content of Cr and Ni increases downstreamfrom ACNA and the pollution due to the two metals is mainlypresent at intermediate distances from ACNA, while no particu-lar trend can be detected for what regards the distance from theriverbed.

The third PC accounts for the contribution to pollution mostlygiven by Pb, Cu and Sn. Samples in position (1,1) and (6,6)(AL13) show the largest content of these pollutants and they arelocated near ACNA and the riverbed (red areas inFig. 3c). Thisbehavior shows that Pb, Cu and Sn pollution is largest at a smalldistance from the ACNA site and that it involves principally theriverbed.

PC4 is related to As and Cd pollution. Sample in (6,6) andgroups D and E show the largest content of As and the mini-mum content of Cd, while the samples in (1,6) and groups Band C show an opposite behavior. The most evident As pollu-tion is present near and at small distances from ACNA, whiledifferent behaviors are observed with respect to the distancefrom the riverbed. Cd is instead maximum near the riverbed andat medium-large distances from ACNA. As and Cd show thusa different behavior with respect to the geographic variables.Finally, PC5 accounts for Hg pollution: it is maximum for Fsamples (AL7, AL18), and for those in (1,1) and (6,1). Hg isl malld

4

sizea it-t andi am-p Thea min-e am-p und,a eralld

for-m

- plesdif-

- theinlydueCNAtion

- u,elow

CNA and from the riverbed asz-values.The surface plots were calculated on the basis of the

les co-ordinates rather than on the network weights: inay meaningful behaviors are more easily detectable; innly the variables (PCs) that exhibit a smooth curve,tted by the surface, should be interpreted. In this caselots show a good fitting and seem to contain meanin

nformation.According to the information carried by PC1 (Fig. 6a), the

argest negative values are recorded for samples in positionAL1, AL2); then, the scores rapidly increase towards pos6,1) (AL17) but the largest positive values are recordedamples in position (1,1) and for groups B and D. Therehe samples in (1,6) are characterized by the largest amoand, Ill Mu, Kao and As; these samples are located upst

rom ACNA and at a distance from the riverbed of about 8samples located in the blue area inFig. 3a). The samplesosition (1,1) (S16, AL26) and samples belonging to ground D show the largest content of Clay, SmeVe, Ni and Vb;

hese samples show the largest adsorbment ability and aocated downstream from ACNA: group D is the most disrom the riverbed, while group B and samples in (1,1) are locn the riverbed (red area ofFig. 3a). The second PC shohe largest negative values for samples in (1,6) and grouhich show large content of Silt, SO TQ and Zn and smamount of Sand, Cr and Ni: these samples are riverbed saexcept for those in (1,6)) and they are located before ACupstream; samples in (1,6)) or immediately after (downstrroup B: samples at the bottom left ofFig. 3b). Groups C–Fnd samples in (1,1) are characterized by an opposite beh

.e. large content of Cr and Ni. They are located at inter

-

t

l

)

f

ll

,

s

;

r,

arger at larger distances from ACNA and at intermediate-sistances from the riverbed.

. Conclusions

The investigation of the sediment composition and grainlong the Bormida river, in Northern Italy (Piedmont), perm

ed to identify the possible presence of metal pollutionts relationship with the mineralogical fractions. The 31 sles were taken both in the riverbed and on its banks.nalysis focused on the identification of the differences inral/pollutant relationships existing between S and AL sles. Since interesting and complex relationships were foprincipal component analysis was performed on the ov

ataset.PCA allowed the identification of the main sources of in

ation:

PC1 accounts for the mineralogical nature of the sam(differences in their mineralogical composition) and theirferent adsorption ability.PC2 accounts for the content of Zn (preferentially bound tosilt fraction), negatively correlated to Cr, Ni and Sn (mabound to the sand fraction). Cr and Ni pollution seemsto two possible sources: one related to the presence of Aand to river flow effects; the other being a source of pollulocated downstream of the ACNA site.PC3 accounts for the contribution to pollution given by CSn, Pb and the organic matter contained in the fraction b63�m.


- PC4 accounts for the differences in Cd and As content of thesamples. Cd and As seem to due to a source different from thepresence of the ACNA site.

- PC5 identifies Hg as another source of pollution, not relatedto the others and not related to the presence of ACNA.

The differences between S and AL samples were observed alsoin PCA results: the two groups of samples are well separated bymeans of PC1 and PC4, showing that their difference is mainlydue to their content of Cd and As: the different mineralogicalnature of the two groups of samples is responsible for the dif-ferent adsorption of these pollutants.

Relationships between the principal components and the dis-tance of the samples from the riverbed and from the ACNA sitewere also considered.

The differences existing between the samples were furtherinvestigated by means of Kohonen self-organising maps, whichallowed a clear clusterization of samples and the identification ofthe principal components (and eventually the original variables)characterising each identified group and some outlier samples.Moreover, local similarity emerged as an important contributionto the data structure, in particular it permitted to identify smallgroups of samples whose similarity was related to few specificdescriptors:

- Group B, made up of S samples. It is located downstream fromy a

ents

- NAIt isnt ounts

- rmeizedlow

- NAizedAs,low

- atlargedi-.

- sorpd ance

- lity,andfar

- orp-s of

all the other metals. They are located at intermediate distancesfrom the riverbed and upstream from ACNA.

- Sample in (6,6) (AL13), showing intermediate adsorption abil-ities, high contents of Pb, Cu, Sn and small amounts of Hg, Crand Ni. It is located near ACNA (downstream) and at inter-mediate distances from the riverbed.

The results obtained by PCA and K-ANNs are in good agree-ment for what regards the identification of trends of the mainpolluting sources with respect to the distance from the ACNAsite and the riverbed. However, K-ANNs appear more power-ful in identifying local similarities. The use of both these toolsis recommended to provide a complete description of the areaunder investigation and to identify the possible sources of con-tamination with greater accuracy. The proposed approach basedon both PCA and K-ANNs application can thus be generallyadopted as a multivariate tool for environmental data analysis.

Acknowledgement

Prof. Emilio Marengo is supported by Regione Piemonte,Ricerca Scientifica Applicata (CIPE 2003).

References

.G.

0)

6–7)

re 41

53–

entice

Hall,

[[ ental

[[ 02)

[ uf-

[ ons,

[ uf-

[ ons,

[ 998)

[ tell.

[[ om.

ACNA but at small distances from it. It is characterized blarge adsorption ability, high content of Cd and low contof Cr, Ni, Pb, Cu, As and Hg.Group D, located at intermediate distances from AC(downstream) and the most distant from the riverbed.characterized by a large adsorption ability, a high conteCr, Ni, As, intermediate content of Pb and Cu and low amoof Cd and Hg.Group C, made up by riverbed samples located at an intediate distance from ACNA (downstream). It is characterby large adsorption abilities, high levels of Cr, Ni, and Cd,amounts of Pb, Cu, Sn, As and Hg.Group E, located at intermediate distances from AC(downstream) and far from the riverbed. It is characterby an intermediate adsorption ability, a high content ofCr and Ni, intermediate amounts of Pb, Cu, Sn, Hg and acontent of Cd.F samples, located far from ACNA (downstream) andintermediate distances from the riverbed. They show aadsorption ability, high contents of Cr, Ni and Hg, intermeate levels of Cd and As and small amounts of Pb and CuSamples in (1,1) (S16, AL26), characterized by a large adtion ability, high contents of Cr, Ni, Pb, Cu, Sn, Cd, Hg ansmall content of As. They are located at intermediate distafrom ACNA (downstream) and near the riverbed.Sample in (6,1) (AL17), showing a large adsorption abilarge amounts of Cd and Hg, quite large amounts of CrNi and small contents of Pb, Cu, Sn and As. It is locatedboth from the riverbed and the ACNA site.Samples in (1,6) (AL1, AL2), characterized by a low adstion ability, intermediate values of Cd and small content

f

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