Multivariate analysis of contamination in the mining district of Linares (Jaén, Spain)

Applied Geochemistry 23 (2008) 2324–2336

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Multivariate analysis of contamination in the mining district of Linares (Jaén, Spain) J. Martínez López a,*, J. Llamas Borrajo b, E. De Miguel García b, J. Rey Arrans c, Ma C. Hidalgo Estévez c, A.J. Sáez Castillo d a

Departamento de Ingeniería Mecánica y Minera de la Escuela Politécnica Superior de Linares, Universidad de Jaén, Spain Departamento de Ingeniería Química de la Escuela Superior de Ingenieros de Minas de Madrid, Universidad Politécnica de Madrid, Spain c Departamento de Geología de la Escuela Politécnica Superior de Linares, Universidad de Jaén, Spain d Departamento de Estadística e Investigación Operativa de la Escuela Politécnica Superior de Linares, Universidad de Jaén, Spain b

a r t i c l e

i n f o

Article history: Received 24 April 2006 Accepted 7 March 2008 Available online 29 April 2008 Editorial handling by A. Danielsson

a b s t r a c t Historically, a significant level of mining activity has taken place in the batholite-related metalogenic enclave of Linares (Jaén province, Spain), associated with Pb–Ag, Cu, Zn and Fe sulphides and Ba sulphate mineralization, though mining here has now been abandoned. Additionally, the area features a significant amount of urban, industrial and agricultural activities. These considerations, taken together, explain the need to assess the levels of concentration of trace elements and to determine their relationship with geogenic and anthropogenic factors. For geochemical characterisation of the soil, the region has been divided into 126 grid squares with an area of 1 km2. For each grid square, 32 trace elements have been analysed. Elemental concentrations of Cu, Pb, Zn, As and Mn have been included in statistical analyses. According to the reference levels established by the Regional Government (Junta de Andalucía), soils in a large part of the study area require amendment applications. The comparison of the mean content for each grid square with the reference levels reveals a significant degree of contamination of the soil by Cu (719 mg kg 1), Pb (22,964 mg kg 1) and As (100 mg kg 1) in those grid squares affected by metallurgic activities. By means of factor analysis, four scores have been identified which together account for 80% of the variance observed. The first score is highly correlated with the logarithms of the variables Fe, Th, La, Ti, Al, Na, K, Zr, Y, Nb, Be and Sc. It is a ‘‘natural” factor that indicates the type of soil matrix (fundamentally granites and, to a lesser degree, Triassic materials). The second score shows high correlation with the logarithms of the variables Mo, Cu, Pb, Zn, Ag, Co, Mn, As, Cd, Sb, Ba, W and Sn, and is the ‘‘metallization” factor related to the mineralization that has been exploited. The third score is mainly determined by the logarithms of the variables Sr, Ca and Mg. This is a ‘‘natural” factor that indicates a type of carbonate soil matrix (Miocene). Finally, the fourth factor groups the logarithms of the variables Ni, V and Cr, elements that are associated with the combustion of fossil fuels. Analysis of the patterns of each of the factors identified enabled achieving a global characterisation of the study area. Cluster analysis of the observations showed there to be five clusters relating to the grid squares, differentiated by lithologies and degrees of contamination. These clusters are used to determine the background of granite and to calculate the anomalous load. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction

* Corresponding author. Tel.: +34 953648528; fax: +34 953648506. E-mail address: [email protected] (J. Martínez López). 0883-2927/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeochem.2008.03.014

The Linares mining district (Spain, Fig. 1) is characterised by the exploitation of galena, associated with sphalerite, chalcopyrite and barite (Azcárate and Argüelles, 1971;

J. Martínez López et al. / Applied Geochemistry 23 (2008) 2324–2336

2325

Fig. 1. Location map, outcropping lithologies (modified from Azcárate, 1977) and grid square distribution in the Linares region.

Azcárate, 1977; Fontboté, 1982; Lillo, 1992, 2003). An important mining industry developed over various centuries, this activity also producing a large quantity of waste materials, which accumulated in the vicinity of the exploitation sites (Gutiérrez-Guzmán, 1999). In parallel with this, a mineral extraction industry came into being, with the creation of numerous gravimetric and flotation washeries. The mining and the mineral extraction activities generated different types of waste materials; on the one hand, a large number of waste tips were produced, featuring large, heterogeneous particles from the tunnelling of galleries and re-deepening of wells; others were characterised by medium-sized particles derived from the materials rejected by the gravimetric process and dumped on waste tips, together with finer materials ejected from the flotation process and deposited in slurry pools. All these rejected materials are located in the vicinity of the extraction shaft and around the concentration plants (Gutiérrez-Guzmán, 1999; Martínez, 2002). The final concentrate was treated in a metallurgic process that, in turn, generated its own solid, liquid and gaseous waste materials which accumulated beside the smelters. In addition to the mining, mineral processing and metallurgical activities described, a significant degree of urban, industrial and agricultural activity is currently present in and around the town of Linares. Among the geogenic fac-

tors that should be considered in determining the geochemical characterisation of the study area, the mining zone is located over a substrate in which various geological units can be differentiated: Palaeozoic granites, Triassic detritic facies and Miocene marls (Fig. 1). Despite all these human activities and the geogenic variability described, no detailed studies have been made to establish the concentrations of contaminant elements in the soil. There is a real need to evaluate the concentrations of trace elements in the soil in the Linares region, as an item of general and reference interest for public authorities and administrative bodies. In this sense, there are numerous previous studies in which multivariate statistical methods have been used to identify the effects of anthropogenic sources of trace metals, over and above the natural geochemical background (Sánchez Gómez and Ramos Martín, 1987; Einax and Soldt, 1998; De Miguel et al., 1997, 1999; Gallego et al., 2002; Menjivar et al., 2000; Yongming et al., in press).

2. Materials and methods For systematic sampling, the mining district of Linares was divided into 126 grid squares, with an individual area of 1 km2 (Fig. 1). In a previous study, three mapping units were taken for a pilot sampling exercise. The Visman

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method was applied for the design of a tactical campaign to determine the mass and number of sampling increments, as described in Martínez (2002) and Martínez et al. (2007a). According to the pilot study, five samples weighing 1500 g were randomly taken from each grid square. In the laboratory, the physical preparation of the five samples consists of drying, mixing, homogenising, crushing and sieving. The latter process was carried out using plastic sieve sets with a mesh of 2 mm and 63 lm. An aliquot of 1 g >63 lm of each sample was subjected to chemical attack in an open teflon reactor with a mixture of 5 mL of HNO3, 10 mL of HClO4 and 10 mL of HF (Ordoñez et al., 2003; Ferreira-Baptista and De Miguel, 2005). After evaporation to dryness the residue was redissolved with 5 mL of HCl and diluted to 100 mL with distilled water. The solution was analysed by inductively coupled plasma-atomic emission spectrometry (ICP-AES). The samples were analysed in the Acme Analytical Laboratories Ltd. (an ISO 9002 accredited company) of Vancouver (Canada). In all, 32 elements were analysed, of which Cu, Pb, Zn, As and Mn are highlighted in the discussion as the main elements associated with human activity. For the statistical treatment of these variables using multivariate techniques, a log-transformation was carried out. Only some of the variables show a normal or log-normal data distribution. For this reason, statistical methods which are not based on that assumption have been applied. Table 1 summarises the results for the 32 elements analyzed: mean, median, standard deviation, minimum, max-

imum, first quartile (Q1) and third quartile (Q3) values for the original data are given. Although Ag, Cd and Sb have been included in the statistical analysis, in several samples these elements were below the detection limit (Table 2). So, they have not been considered in the conclusions of this study. Factor analysis makes it possible to simplify the explanation of the set of observations by reducing the dimensionality. To achieve this, it is necessary to fit a kdimensional subspace to the space of m dimensions (m: number of variables) in which the set of n individuals is located, thus obtaining the most accurate representation possible of the relationships existing among the cloud of individuals. This subspace is made up of k vectors such that these contain the greatest amount of information possible on the aspect in question. Thus, an explanation is sought of the relationships between the original variables, making use of the existence of a smaller number of underlying variables, termed factors, which explain the common variability of these original variables. The principal components method was used to extract the factors. Varimax orthogonal rotation was applied in order to clarify the interpretation of factors. The factor loadings obtained had either very high or very low values, with the intermediate values being eliminated, which facilitated the subsequent process of interpretation. Factor scores were estimated by the Anderson-Rubin method: these scores represent estimates of the levels of each grid with respect to each factor. Factor analysis and derivative methods have been widely

Table 1 Statistical data: mean, median, minimum, maximum, first quartile (Q1) and third quartile (Q3) values are expressed in mg kg

1

Variable

Observations

Mean

Median

Standard deviation

Min.

Max.

Q1

Q3

Ag Al As Ba Be Ca Cd Co Cr Cu Fe K La Mg Mn Mo Na Nb Ni P Pb Sb Sc Sn Sr Th Ti V W Y Zn Zr

126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126

2.028 63,048 26.11 875.4 3683 35,865 0.763 12,299 49.94 145.4 32,024 25,312 67.89 6194 1210.7 1.78 4820 9563 20.589 848.3 4077 11.49 11,604 5.602 149.85 25.6 3259.2 72.19 5.954 22.013 123.9 103.83

0.25 56,400 17 705.5 4 14,875 0.2 10 49 53.5 30,900 24,650 69 5875 1086 1.5 3200 8 20.75 765 1279 2.5 10 4 122 21 3000 71.25 4.5 19 71.5 100

4.299 19,728 29.23 695.4 1.453 44,882 2.867 6.261 15.37 242.5 8863 6803 22.88 2372 802.1 1.54 3619 4.451 5.962 441.4 7016 22.44 4.072 6.05 89.47 15 1097.7 13.44 7.91 8.905 206 32.76

0.25 30,100 2.5 120 0.5 1700 0.2 5 26 14 15,900 10,100 17 2500 194 1 1500 5 10 330 27 2.5 6 1 60 4 1244 43 2 9 26 36

29.1 114,200 175.5 3728.5 6 207,600 31.88 39 150 1654 59,200 38,450 145 12,450 3981 14.5 16,400 24 42 2920 37,356 186 25 52.5 530.5 80 6400 122.5 72.5 48 1988.1 238

0.25 48,800 11 369.5 2.15 6600 0.2 8 40 32.7 25,512 19,700 52.75 4387 530.8 1 2200 7 15.5 578.8 267 2.5 9 2 103 17 2575 64 2 15 46 86

1.7 74,200 26 1024.2 5 41,125 0.6 14.25 56.25 128.4 36,562 31,662 82.25 7625 1608 2 6363 11.5 25 982.5 4866 10 13 7 158 31.5 3662.5 79 7 28 133.5 117

Table 2 Total concentration (in mg kg

1

) for Cu, Pb, Zn, Ag, Mn, As, Cd and Sb in the 126 grid squares

Pb

Zn

Ag

Mn

As

Cd

Sb

Square

Cu

Pb

Zn

Ag

Mn

As

Cd

Sb

18.5 32.0 33.0 20.0 69.0 54.0 55.0 107.0 272.0 1654.0 507.5 49.0 26.0 34.0 22.0 27.0 20.0 22.0 37.5 637.5 178.5 203.0 240.5 188.0 646.0 94.0 69.0 23.0 38.5 31.0 215.5 42.0 742.5 190.0 691.5 173.0 786.0 998.0 195.0 427.0 318.0 34.5 42.0 43.0 359.0 129.5 75.0 53.0 210.0 43.5

62.5 269.0 119.0 247.0 1288.0 1741.0 3741.0 5018.0 11,606.0 6134.0 12,305.5 3404.0 1772.0 1149.0 73.0 86.0 330.0 464.0 6737.5 4059.5 6600.0 7547.0 11,721.5 5802.0 35,207.0 3559.0 8671.0 981.0 1102.0 473.0 10,014.5 3932.0 37,356.0 5202.0 19,895.0 9487.0 11,026.0 9898.7 5577.0 21,675.0 12,978.0 1064.5 93.0 3877.0 1401.0 7050.5 2533.0 2864.0 4136.5 1830.0

44.0 47.0 40.0 80.0 114.5 152.0 139.0 125.5 225.0 437.0 308.0 148.0 138.0 132.0 46.0 42.0 48.0 93.0 110.5 115.0 101.0 192.0 222.5 166.0 228.0 144.0 156.0 115.0 52.0 29.0 79.0 89.0 152.0 54.0 116.5 92.0 267.0 1988.0 92.0 953.0 215.0 98.0 41.0 31.0 46.0 96.0 64.0 46.0 449.5 64.0

<0.5 <0.5 <0.5 <0.5 <0.5 1.3 3.3 1.2 5.7 2.8 4.8 <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 1.3 <0.5 1.5 3.2 3.6 2.2 11.0 0.8 4.7 <0.5 <0.5 <0.5 3.7 1.0 29.2 1.0 10.1 4.2 4.5 24.3 1.7 6.6 5.3 <0.5 <0.5 1.1 <0.5 2.0 0.5 1.0 2.4 0.7

311.5 375.0 384.0 1482.0 1065.0 1227.0 920.0 1952.5 2395.0 1511.0 3141.0 1409.0 925.0 944.0 254.0 228.0 765.0 1180.0 1067.0 1161.0 805.5 2385.0 3598.0 1677.0 2897.0 1628.0 1089.0 1141.0 326.5 647.0 1987.0 2061.0 1617.0 1409.0 2227.5 1618.0 2893.0 1514.8 2189.0 1599.0 1694.0 1388.0 308.0 1579.0 1565.0 2530.5 1998.0 2219.0 2014.0 970.5

9.5 6.0 7.0 22.0 18.5 21.0 26.0 27.5 65.0 44.0 131.0 17.0 13.0 26.0 9.0 6.0 10.0 17.0 85.5 23.5 82.5 54.0 71.0 27.0 87.0 18.0 16.0 23.0 9.5 6.0 50.0 35.0 160.5 21.0 49.5 26.0 54.0 175.5 31.0 47.0 49.0 18.5 8.0 16.0 16.0 41.5 34.0 17.0 24.0 11.0

<0.4 <0.4 <0.4 <0.4 <0.4 <0.4 <0.4 0.9 1.3 2.0 1.8 1.2 0.4 0.4 <0.4 <0.4 <0.4 <0.4 <0.4 <0.4 0.9 1.1 1.9 1.8 2.7 1.0 0.9 0.5 0.4 <0.4 0.4 0.4 1.1 0.4 0.7 0.8 4.6 31.8 0.7 3.0 0.9 <0.4 <0.4 <0.4 0.5 0.7 <0.4 <0.4 2.7 <0.4

<5 <5 <5 <5 <5 <5 <5 8 82 10 28 5 <5 <5 <5 <5 <5 <5 7 <5 7 50 35 17 38 <5 9 <5 <5 <5 16 6 65 5 47 25 38 992 11 23 14 <5 <5 <5 <5 12 8 9 15 7

64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113

255.0 189.0 94.0 74.0 117.0 50.0 22.0 38.0 18.0 31.0 68.0 20.5 128.0 48.5 68.0 77.5 121.0 81.0 69.0 36.0 32.0 49.5 43.0 48.0 38.0 52.0 109.5 332.0 66.0 78.0 101.0 70.0 40.0 28.0 23.0 57.0 19.0 33.0 22.0 92.0 155.0 191.0 110.0 90.0 65.0 35.0 29.3 19.0 24.0 42.5

3905.0 13,421.0 4816.0 1470.0 653.0 308.0 158.5 134.0 281.0 614.0 567.0 951.5 4205.0 1208.5 1828.0 2833.5 1894.0 1442.0 498.0 152.0 116.0 158.5 692.0 1511.0 374.5 1167.0 2560.0 31,544.0 3623.5 1270.0 2269.0 1052.0 201.0 84.0 45.0 401.0 176.0 260.0 450.0 1829.0 10,096.0 5881.0 2011.0 1093.0 1399.0 549.0 175.9 37.0 33.0 70.0

199.0 69.0 57.0 51.0 43.0 32.0 40.0 36.0 29.0 68.0 63.0 82.5 41.0 43.0 66.0 67.0 171.0 82.0 74.0 55.0 48.0 53.0 44.0 60.0 40.0 40.0 41.0 161.0 96.5 115.0 175.0 113.0 54.0 43.0 45.0 67.0 43.0 36.0 36.0 139.0 168.0 199.0 749.0 236.0 202.0 80.0 51.6 37.0 40.0 49.5

2.0 5.8 1.5 <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 1.7 0.6 0.7 0.8 0.7 0.6 <0.5 <0.5 <0.5 <0.5 0.9 0.6 <0.5 0.7 3.7 18.7 1.0 <0.5 1.3 0.5 <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 0.7 5.1 3.5 2.1 1.3 0.8 <0.5 0.5 <0.5 <0.5 <0.5

1216.0 1515.0 1807.0 2346.0 2246.0 1100.0 654.0 194.0 896.0 3044.0 3981.0 1571.0 1236.0 869.0 2340.0 1492.0 1770.0 1459.0 1618.0 322.0 283.0 266.5 991.0 1188.0 1554.5 1077.0 1026.5 1605.0 635.0 693.0 1083.0 740.0 275.0 259.0 345.0 301.0 469.0 810.0 755.0 633.0 1318.0 1429.0 954.0 644.0 690.0 398.0 299.8 416.0 362.0 287.5

17.0 18.0 16.0 12.0 23.0 11.0 7.0 8.0 7.0 13.0 20.0 8.5 30.0 12.0 18.0 14.5 15.0 21.0 18.0 13.0 13.0 9.0 10.0 26.0 11.0 10.0 19.5 160.0 20.5 23.0 24.0 23.0 15.0 14.0 11.0 15.0 9.0 14.0 15.0 20.0 24.0 32.0 16.0 22.0 19.0 12.0 10.0 2.5 9.0 11.0

0.7 <0.4 <0.4 <0.4 <0.4 <0.4 <0.4 <0.4 <0.4 0.4 0.7 <0.4 <0.4 <0.4 0.4 <0.4 0.6 <0.4 <0.4 <0.4 <0.4 <0.4 <0.4 0.6 0.4 <0.4 <0.4 1.6 <0.4 <0.4 <0.4 0.6 <0.4 <0.4 <0.4 <0.4 <0.4 <0.4 <0.4 <0.4 0.6 <0.4 0.6 0.7 <0.4 <0.4 1.0 <0.4 <0.4 <0.4

14 29 10 5 5 <5 <5 <5 <5 <5 <5 <5 12 6 7 9 6 <5 <5 <5 <5 <5 <5 <5 <5 7 13 186 16 <5 <5 <5 <5 <5 <5 <5 <5 <5 <5 5 18 16 6 5 8 5 5 <5 <5 <5

2327

Cu

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

J. Martínez López et al. / Applied Geochemistry 23 (2008) 2324–2336

Square

33.0 50.0 41.0 71.0 64.5 30.0 23.0 39.0 25.5 57.0 36.5 29.0 31.0

42.0 107.0 144.0 169.0 174.0 244.0 325.0 1451.0 206.5 358.0 96.5 98.0 26.5

46.0 35.0 27.0 47.0 63.0 88.0 51.0 78.0 176.0 56.0 67.5 52.0 41.5

<0.5 <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 0.5 <0.5 <0.5 <0.5 <0.5 <0.5

324.0 444.0 661.0 534.0 452.5 543.0 666.0 674.0 464.0 292.0 391.0 355.0 292.0

10.0 7.0 8.0 15.0 11.0 11.0 18.0 24.0 11.0 11.0 7.5 10.0 9.5

<0.4 <0.4 <0.4 <0.4 <0.4 <0.4 <0.4 0.5 <0.4 <0.4 <0.4 <0.4 <0.4

<5 <5 <5 <5 <5 <5 <5 <5 <5 <5 <5 <5 <5

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used in geochemical applications to identify pollution sources and to apportion natural vs. human contributions (Ratha and Sahu, 1993; García et al., 1996; Einax and Soldt, 1998; Facchinelli et al., 2001; Chen et al., 2005). Cluster analysis was used to separate the individuals into natural groups, based on the matrix of concentration values (log-scale). It was decided not to base this analysis on factor scores (to avoid the problem of multicollinearity), due to the averaging and scaling effects that factors introduce in the analysis of the original variables. By means of proximity criteria, the discrepancies between the individuals in a single group were minimised while the distances separating the groups from each other were maximised. Ward clustering criterion and the measure of the Euclidean distance were applied to measure the divergence. The agglomerative hierarchical clustering was used, separating in each step, from both the groups and the conjunctions of individual specimens, the two groups that are most similar, until a single group is arrived at. Examples of applications of these methods to sediments are common (Huang et al., 1994; Kalogeropulos et al., 1994; Ruiz et al., 1998; Facchinelli et al., 2001; Yongming et al., in press).

3. Results

51 52 53 54 55 56 57 58 59 60 61 62 63

441.5 186.0 744.0 116.0 43.0 21.0 46.5 24.7 32.0 58.5 14.0 38.5 1154.0

36,125.0 8416.0 13,200.0 6802.0 1697.0 412.0 120.0 383.0 670.0 2267.0 524.0 1172.0 11,811.0

129.5 100.0 127.0 118.0 109.0 47.0 43.0 26.0 44.0 88.0 64.0 74.0 158.0

16.5 2.7 6.1 0.6 <0.5 <0.5 <0.5 0.6 <0.5 0.6 <0.5 <0.5 13.0

3475.0 1562.0 2373.0 1382.0 869.0 1139.0 269.5 521.2 1476.0 1397.5 1035.0 1493.5 1732.0

48.5 23.0 55.0 30.0 15.0 13.0 10.5 10.0 15.0 24.5 20.0 15.0 73.0

0.8 0.4 0.8 <0.4 <0.4 <0.4 <0.4 1.0 <0.4 <0.4 <0.4 0.5 1.1

57 13 28 10 <5 <5 <5 5 <5 <5 <5 <5 73

114 115 116 117 118 119 120 121 122 123 124 125 126

3.1. Reference levels Mean values obtained for each grid square in the study area have been compared with the reference levels established by current regulations applicable in the region (Comunidad Autónoma de Andalucía, Llamas et al., 2000), as representing the threshold of soil contamination. It makes it possible to identify the squares that must be treated, depending on the type of use to be made of the ground. The elements compared with the reference levels are Cu, Pb, Zn, Mn and As, due to their relationship with the metallization factor, as discussed below. In the case of Cu, there are 10 grid squares where soil would necessarily have to be treated if agricultural use were contemplated or if a forestry application were considered (mean content of Cu higher than 500 mg kg 1). Three grid squares would require such treatment if the proposed use were industrial (mean content of Cu higher than 1000 mg kg 1). In the case of Pb, the comparison of the mean content with the reference levels reveals a significant degree of contamination of the soil. Of the 126 grid squares studied, 81 would require treatment if agricultural use were planned (mean content of Pb higher than 500 mg kg 1), 73 would have to be treated if forestry use were considered (mean content of Pb higher than 1000 mg kg 1) and action would have to be taken in 50 grid squares if the land were intended for industrial use (mean content of Pb higher than 2000 mg kg 1). For Zn, the reference levels established are generally higher than the mean contents recorded, and so only in one grid square would it be necessary to treat the soil if agricultural use were considered (mean content of Zn higher than 1000 mg kg 1) and also in the case of proposed forestry use (mean content of Zn higher than 1000 mg kg 1).

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Distance 135

1/2

2/2

90

1/3

2/3

3/3

45 1/5

3/5

2/5

4/5

5/5

0

OBSERVATIONS (n - 126) Fig. 2. Dendrogram obtained for cluster analysis of observations.

Table 3 Cluster analysis of observations. Land use, geological time and lithology of the grid squares are included Cluster 2

Cluster 5

Land use – activities

Cluster 9

Geological time

Lithology

Squares

1/2

1/5

Mining

1/9

Quaternary and Palaeozoic

6, 7, 27, 12, 26, 20, 8, 32, 34, 54, 19, 21, 46

2/5

Mineral processing Metallurgy Dehesa Pasture (Mining)

2/9 3/9

Triassic Palaeozoic and Triassic

Graniticcolluvium and granite Lutites Granite and lutites

49, 106, 36, 24, 39, 64, 31, 65, 52, 104, 105 9, 22, 23, 10, 40, 41, 11, 37, 33, 35 25, 51, 53, 63, 91, 38

4/9 5/9

Granite Lutites Granite

5, 55, 60, 13, 14, 28, 18, 61, 42, 62, 75 45, 87, 74, 4, 56, 59, 88, 73, 67, 68, 82

Lutites

50, 77, 76, 90, 47, 48, 78, 79, 89, 92

3/5

2/2

Mining (spoil tips) Mining -Urban

6/9

Palaeozoic Triassic and Palaeozoic Triassic

7/9

Triassic

Lutites

44, 66, 80, 81, 93, 103, 86, 121, 94, 95, 107, 108

4/5

Olive groves

8/9

Lutites Marls

1, 3, 100, 69, 120, 70, 85, 119, 2, 30, 17, 72, 101, 102, 116

5/5

Cultivation Urban

9/9

TriassicMiocene contact Miocene

Marls

15, 43, 115, 16, 57, 113, 71, 98, 126, 112, 114, 111, 58, 110, 83, 96, 117, 118, 29, 99, 109, 84, 97, 124, 125, 122, 123

No action would be necessary if the land were intended for industry (mean content of Zn higher than 3000 mg kg 1). For As, treatment would be necessary in 14 grid squares if the land were to be used for agriculture (mean content of As higher than 50 mg kg 1), and in four cases if forestry use were proposed (mean content of As higher than 100 mg kg 1). No action would be necessary in any case for industrial use (mean content of As higher than 300 mg kg 1). 3.2. Cluster analysis From a statistical point of view, the dendrogram (Fig. 2) clearly shows two clusters, the first one composed of 84 grids (cluster 1/2) and the second one with 42 grids (cluster 2/2). However, these two main clusters comprise very heterogeneous grid squares when considering lithologies and activities. Therefore, both clusters can be divided into subclusters, reflecting more natural groupings. In this way, it is interesting to analyse the distribution of the variables into 5 and 9 subclusters.

In Table 3, the groupings in 2, 5 and 9 clusters are reported and activity, geological time and lithology for each one are also included. Table 4 shows mean and median values for the 9 clusters, which show different degrees of soil affects. It is worth noting that the local geochemical background for granite has been calculated from grid squares grouping in cluster 4/9. As shown in Table 3, cluster 1/2 (subdivided into 1/5, 2/ 5 and 3/5) groups grid squares affected by mining. On the other hand, cluster 2/2 (subdivided into 4/5 and 5/5) contains grid squares mainly linked to agricultural activities. Cluster 1/5 contains grid squares where mining activity has taken place and is subdivided into 1/9 and 2/9. Cluster 1/9 is composed of grid squares located over granite or granitic colluvium while cluster 2/9 is found over Triassic lutites. Cluster 2/5 also includes grid squares where mining activity is known to have occurred. Within this cluster, the grid squares 25, 51, 53, 63, 91 and 38 can be distinguished. Here, either the most important activity was that of metallurgy or the grid squares are clearly influenced by

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Table 4 Mean and median contents of total elements (expressed in mg kg Variable mg kg 1

126 squares

1

) for clusters

Cluster 1/5

Cluster 3/5

Cluster 4/5

Cluster 5/5

2/9 Triassic

3/9 Palaeozoic and Triassic

4/9 Paleozoic

5/9 Triassic– Paleozoic

6/9 Triassic

7/9 Triassic

8/9 Triassic and Miocene contact

9/9 Miocene

Granitic colluvium Granite

Lutites

Granite and lutites

Granite

Lutites–granite

Lutites

Lutites

Lutites

Marls

Dehesa

Pasture-mining

Mining

Mining-Urban

Cultivation

Cultivation and urban

Mining

Ag Al As Ba Be Ca Cd Co Cr Cu Fe K La Mg Mn Mo Na Nb Ni P Pb Sb Sc Sn Sr Th Ti V W Y Zn Zr

Minerallurgy

Metallurgy

Mean

Median

Mean

Median

Mean

Median

Mean

Median

Mean

Median

Mean

Median

Mean

Median

Mean

Median

Mean

Median

Mean

Median

Mean

Median

2.02 63,048 26.11 875.4 3.68 35,865 0.76 12.29 49.94 145.4 32,024 25,312 67.89 6194 1210.7 1.78 4820 9.56 20.58 848.3 4077 11.49 11.6 5.6 149.85 25.6 3259.2 72.19 5.95 22.01 123.9 103.83

0.25 56,400 17 705.5 4 14,875 0.2 10 49 53.5 30,900 24,650 69 5875 1086 1.5 3200 8 20.75 765 1279 2.5 10 4 122 21 3000 71.25 4.5 19 71.5 100

1.47 95,630.7 34.19 1283.58 5.15 98,92 0.56 14.19 40.65 135.3 42,507.6 33,607.6 99.23 7003.85 1433.96 1.65 10,534.6 17.46 15.65 1121.15 5116.73 6.53 18.35 6.53 11,8.96 51.92 5003.85 78.42 5.62 35.23 119.08 153.77

1.15 97,400 26 1003 5 8200 0.4 12 40 94 42,550 34,400 99.5 7150 1382 2 10,400 17.5 14 1070 5018 6 19 7 111 50 5100 78 6 37 118 149

3.23 56,218 26.18 1070 4.18 17,009 0.79 14.27 54.14 188 30,677 22,927 72.91 5014 1589 2.18 3009 7.77 20.18 1066 7159 16.5 10 11 126.6 21.82 3059 67.64 7 20.63 214.8 90.91

2.7 55,000 24 966 4 16,700 0.6 14 49 189 30,800 23,550 74 4750 1562 2 2700 7 22 870 5881 16 10 6 122 21 3100 67.5 5 21 166 90

7.57 85,495 72.5 1836 4.6 21,010 1.82 20.4 40.4 584 42,900 31,905 87.55 6855 2306 3.8 8400 12.9 21.95 1040.5 15,224 39.6 16.15 10.7 141.6 38.65 4300 83.15 21.15 35.45 308.8 121.3

5.12 89,375 54 1917 5 13,650 1.55 20.75 37.75 467 39,650 32,450 80.25 6675 2306 2.75 8800 13.25 20.25 922.5 12,014 37.5 16.5 9 143.5 35 4350 77 11 35.5 223.7 114.5

15 56,657 99.8 1243 3.46 40,331 6.49 26.57 35.28 719 34,916 22,258 60.3 6002 2266 2.63 3814 6.58 27.6 775 22,964 78.5 10.6 11.7 123.9 21.3 2607 63.73 6 27.62 465 83.4

13.93 55,150 80 1377 3.5 35,550 1.35 26.7 36.35 695 35,125 20,450 66 5600 2052 2.5 3550 6 28.5 810 22,372 63.5 10 12 124 19 2650 66.5 6.5 23.5 160 87

0 88,173 18.09 879.9 4.54 8355 0.26 10 42.77 34.82 40,105 34,236 86.59 6505 1182.6 1 10,668 16 13.04 947.3 1212 25 15.77 5.22 104.64 43.5 4777 76 5.04 29.68 100.73 141

0.25 84,000 18.5 816 5 7400 0.2 10 40.5 34 39,100 33,700 86 6100 1141 1 10300 14.5 13 980 1149 2.5 14 5 107 39 4700 79 5 29 98 136

0.28 63,109 17.18 1169 4.59 7141 0.32 13.59 55.64 79.7 34,459 27,173 71 4605 1967 1.63 3095 9.13 22.23 709.5 765 2.95 10.81 3.9 104.3 22 3227 82.55 5.95 19.77 56 104.09

0.25 58,700 16 711 4.5 6500 0.2 12 52 48 31,800 26,700 69 4500 1565 2 2500 9 20 670 614 2.5 10 4 102 22 3100 76 5 19.5 51 106

0. 96 55,130 18.65 825 4.5 4350 0.22 14.35 48.7 72.1 28,830 22,205 78.8 3580 1386 1.5 2765 8.45 19 686.5 2465 9.05 9.15 3.65 125.4 21.05 3170 69.7 7.2 19.9 56. 85 103.2

0.7 55,250 17.5 623 5 4300 0.2 13.5 49 67 28,850 23,300 78 3500 1157 1.5 2700 8.5 19.5 600 2547 7.75 9 3 118 20.25 3075 69 6 19.25 55 102.75

0.84 57,250 19.42 808.3 3.83 45,267 0.35 10.83 65.17 76.42 32,133 24,083 59.42 7242 1064 2.41 2917 7.08 23.5 1306 1924 4.29 10.41 7.83 179 18.08 2633.3 73.25 4.91 19.33 120.3 87.92

0.75 5700 20.5 822.5 4 37,600 0.2 10 59 79.5 32,200 24,100 61.5 6800 866 2 2900 7 25 985 1447 2.5 10 7 176 18.5 2600 74 5 18 114 86.5

0.25 51,397 9.83 461.3 3.33 14,670 0.2 7.9 49.4 29.47 25,373 24,797 62.27 4957 620.2 1.13 3127 7.66 19 630.3 250.6 2.5 9.1 2.83 105 19.77 2786.7 64.37 2.8 15.83 42.87 104

0.25 51,800 9 405.5 4 6700 0.2 8 52 30 25,250 24,600 59 4400 654 1 2500 7 19 580 260 2.5 9 2 107 19 2800 68 2 16 40 100

0.27 44,971 10.31 319 1.56 102,717 0.25 7.15 54.06 37.11 23,397 18,114 39.11 7793 340.7 1.33 2570 6.09 23.65 567.5 190.1 2.77 9 1.66 247 11.84 2228.1 67.89 2.22 13.18 53.34 71.7

0.25 45,300 10 289 2 98,450 0.2 7 55 35 23,100 18,300 40 7900 322 1 2300 6 25 570 116 2.5 9 1 192 11 2200 69 2 13.5 47 73.5

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Cluster 2/5

1/9 Quaternary Palaeozoic

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the proximity of this activity. These grid squares have the highest mean values for Cu (719 mg kg 1), Pb (22,964 mg kg 1) and As (100 mg kg 1) in the study area. Grid squares number 9, 22, 23, 10, 40, 41, 11, 37, 33 and 35 are associated with a significant degree of mining and mineral processing activity, which is confirmed by examination of the field data. The lithology of the squares in cluster 2/5 is not a conditioning factor, as Triassic lutites were observed together with granite (Palaeozoic), granitic colluvium (Quaternary) and mixtures of these. The cluster would indicate a high degree of contamination, with high mean values of the study variables. Cluster 3/5 is a heterogeneous one that features various aspects of interest. A detailed study reveals the existence of several subgroups, such as cluster 4/9, whose grids are all located over granite, where mining activity had taken place and there was a dehesa. For this reason, these grids have been selected to calculate the local background for granite. The subgroup that compose cluster 5/9 is characterised by the predominance of Triassic material over granites, and in some of these grid squares a moderate degree of mining activity had occurred, while others had been used for pasture. Subgroup 6/9 is mainly located over Triassic material where mining has taken place; moreover, some of these areas have been influenced by metallurgical activities and the mean contents are Pb: 2465 mg kg 1, Cu: 72 mg kg 1 and As: 19 mg kg 1. At present, this land is generally dedicated to olive groves, planted on former waste tip sites. The cluster 7/9 is characterised by its location over Triassic material, where some grid squares provide evidence of for-

mer mining activity and present-day agricultural use. Also noteworthy is the fact this subgroup contains all the urban grid squares that are located over Triassic material, with Pb: 1924 mg kg 1; Cu: 76 mg kg 1 and As: 19 mg kg 1. Cluster 3/5 would indicate a low degree of contamination, according to the mean values observed (Tables 3 and 4). Cluster 4/5 (8/9) includes Triassic–Miocene contact zones. Although there is a predominance of Triassic lithology, grid squares 1 and 2 are very evidently located over Miocene material, though close to the Triassic contact zone. In this group, no mining, mineral processing or metallurgical activity was detected, with the land of the grid squares being dedicated to olive groves. Cluster 5/5 (9/9) includes all the grid squares located over Miocene material or where Miocene predominates over other lithologies (i.e. Triassic). Agriculture is the main activity in these areas, with the cultivation of olive trees predominating over that of cereal crops. This group includes grid square 58, which is characterised by mineral processing activity and which is located over Miocene and Triassic materials, with tertiary rocks present to a greater extent than others. This group includes all those sites where urban areas have been constructed. 3.3. Factor analysis Taking into account the scree plot (Fig. 3), four factors have been deduced that account for 80% of the original variance. With the exception of log Mo and log P, the variables have high values of communality (the proportion of vari-

16 15 14 13 12 11 10

Eigenvalues

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9 8 7 6 5 4 3 2 1 0 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

Number of factors Fig. 3. Scree plot in factor analysis.

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ance of such a variable shared with the others and accounted for by common factors). In Table 3, the factor loadings and the communalities of the variables for the four obtained factors are reported. The first factor, that accounts for 32% of the total variance observed, is highly correlated with the logarithms of the variables Fe, Th, La, Ti, Al, Na, K, Zr, Y, Nb, Be and Sc. It is a ‘‘natural” factor that indicates the type of soil matrix, and specifically, soils of a silicate nature (fundamentally granites and, to a lesser degree, Triassic material). The second factor (28% of the total variance observed) shows high correlation with the logarithms of the variables Mo, Cu, Pb, Zn, Ag, Co, Mn, As, Cd, Sb, Ba, W and Sn, and is the ‘‘metallization” factor related to the mineralization that has been exploited. The location and intensity of mining, mineral processing and metallurgical activities have different degrees of effect on the soils over which they are developed. Phosphorus, which has a lower degree of communality, is present in association with the silicate lithology factor and the metallization factor. The presence of this element can be related to agricultural activity. The third factor is mainly determined by the logarithms of the variables Sr, Ca and Mg and accounts for 12% of the total variance observed. This is a ‘‘natural” factor that indicates a type of carbonate soil matrix (Miocene). Finally, the fourth factor (8% of the total variance observed) groups the logarithms of the variables Ni, V and Cr, elements that are associated with the combustion of fossil fuels (Illescas et al., 1996; De Miguel et al., 1998, 1999; Gallego et al., 2002). Box-plots for scores of the grids for the four obtained factors are presented in Fig. 4: Score 1: the distribution is slightly asymmetric, with the box displaced towards the maximum value. The grids 6, 7, 13, 14 and 27 are identified as outlier values. The activity observed in these grid squares is mining and

3

2

6 713 27 14

33 63

11 4

1

0

-1

-2 71

-3 Factor score 1 Factor score 2 Factor score 3 Factor score 4 Fig. 4. Box-plots of factor scores for factor analysis.

dehesa and corresponds to grid squares in which granite (i.e. granite, Triassic-granite, Quaternary-granite) is present as a common lithology. Score 2: it is characterised by a highly asymmetric distribution. The grids 33 and 63 are marked as outliers. Nevertheless, there are some grid squares (9, 11, 22, 23, 25, 33, 35, 37, 38, 49, 51, 53, 63 and 91) with values higher than the 90th percentile, which are noteworthy. They correspond to grids where Triassic or granitic materials outcrop and where intensive mining and/or metallurgical activities have taken place. Score 3: the distribution is symmetric. There are neither outliers nor extreme values. Scores with highest values correspond to grid squares located over Miocene materials where there is agricultural activity and a total absence of mining activity. Score 4: has an almost symmetric distribution, even if the median is a little displaced in the box. The grids 4 and 11 are considered to be outlier values due to their high score. This factor score is linked to the elements Ni, Cr and V. These elements are normally associated with organic matter and are found together in abnormally high concentrations in the immediate vicinity of oil refineries, power plants and other industrial installations where fossil fuels are used (Illescas et al., 1996). In addition, they are also associated with outcrops of geological formations with a high content of organic mat-

Table 5 Factor loadings, communalities and explained variance in factor analysis Variable

Factor 1

Factor 2

Factor 3

Factor 4

Communality

Log Nb 0.928 0.896 Log Al 0.925 0.947 Log Sc 0.896 0.931 Log Na 0.894 0.903 Log Ti 0.884 0.881 Log Th 0.849 0.363 0.954 Log K 0.846 0.778 Log Y 0.816 0.504 0.939 Log Fe 0.792 0.393 0.355 0.913 Log Zr 0.788 0.465 0.883 Log La 0.650 0.309 0.601 0.896 Log P 0.474 0.418 0.529 Log Sb 0.915 0.860 Log Ag 0.914 0.871 Log Cu 0.908 0.827 Log Pb 0.349 0.852 0.888 Log As 0.301 0.849 0.815 Log Co 0.785 0.769 Log Cd 0.764 0.655 Log Mo 0.716 0.526 Log Zn 0.413 0.674 0.718 Log Mn 0.373 0.665 0.435 0.788 Log W 0.310 0.638 0.349 0.691 Log Sn 0.456 0.633 0.620 Log Ba 0.483 0.570 0.599 Log Ca 0.333 0.879 0.886 Log Mg 0.861 0.891 Log Sr 0.727 0.631 Log Be 0.562 0.399 0.566 0.810 Log V 0.407 0.808 0.850 Log Cr 0.798 0.791 Log Ni 0.517 0.657 0.807 *Factor loadings with absolute values lower than 0.3 are omitted. % of variance 32% 28% 12%

8%

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ter. This is the case for grid squares 4 and 11, where phyllites outcrop. All the grids located in the town vicinity have values for factor score 4 which are higher than percentile 80. This is associated with the use of fossil fuels and wind-effected transport.

3.4. Analysis of regional distribution of metallization As shown above, the second factor (that accounts for 28% of the total variance) is mainly linked to the variables Mo, Cu, Pb, Zn, Ag, Co, Mn, As, Cd, Sb, Ba, W and Sn (see Table 5), so it has been termed the ‘‘metallization” factor. Martínez (2002) and Martínez et al. (2007b) have calculated the Enrichment Factors (ER) for several elements in these soils. The ER values indicate how many times the concentrations measured exceed the Clarke values and lead to identification of anomalous contents. Among all

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these elements, those defined as anomalous are Cu, Pb, Zn, Ag, Mn, As, Cd and Sb. For this paper, Ag, Cd and Sb are not considered because more than 50% of the data set are below the detection limit. Fig. 5 shows distribution maps for the 5 elements under study, considering the grid square distribution from Fig. 1. Lead was found to show several outliers, of which 4 are particularly interesting; these are located in areas where a significant amount of metallurgical, mining or mineral processing activity took place (grid squares 25, 33, 51, 91), having Pb levels exceeding 26,000 mg kg 1. The distribution of As also indicatives these outliers, as well as one other in the area with metallurgical and mineral processing (grid square 11, 33, 38 and 91, exceeding 100 mg kg 1). High values of Zn (1988 mg kg 1) and Cu (1854 mg kg 1) appear near metallurgical activity (grid square 38 and 10, respectively). Maximum values for Mn (3961 mg kg 1) are recorded in grid square 74, where granite outcrops and

Fig. 5. Grid maps for discrete values of Cu, Pb, Zn, Mn and As concentration in soils. Mean values in mg kg grey shading corresponds to higher contents.

1

are included in each grid square and intense

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Table 6 Mean, median, standard deviation (StDev), median absolute deviation (MAD), mean + 2StDev, median + 2 MAD values for grid squares included in cluster 4/9, representing local granite background Variable (mg kg

Mo Cu Pb Zn Ag Ni Co Mn Fe As Th Sr Cd Sb V Ca P La Cr Mg Ba Ti Al Na K W Zr Sn Y Nb Be Sc

1

)

Granite (4/9) Mean

Median

1 34.82 1212 100.73 0.28 13.04 10 1182.6 40,105 18.09 43.5 104.64 0.26 2.5 76 8355 947.3 86.59 42.77 6505 879.9 4777 88,173 10,668 34,236 5.04 141 5.22 29.68 15.86 4.54 15.77

1 34 1149 98 0.25 13 10 1141 39,100 18.5 39 107 0.2 2.5 79 7400 980 86 40.5 6100 816 4700 84,000 10,300 33,700 5 136 5 29 14.5 5 14

StDev

MAD

Mean + 2StDev

Median + 2MAD

0 16.82 534 23.35 0.12 2.91 1.62 243.9 7707 5.2 9.48 14.69 0.11 0 13.69 2862 215.1 10.23 11.35 1772 211.2 728 11,491 2248 2095 1.03 23.48 0.78 2.77 3.57 0.82 4.24

0 11 198 17 0 1.5 1 216 6450 3.5 10 8 0 0 12 1700 210 10 6.5 900 54 300 2600 1300 1950 1 15 1 2 1.5 1 2

1 68.46 2280 147.43 0.52 18.86 13.25 1670.4 55,519 28.49 62.46 134.02 0.48 2.5 103.38 14,079 1377.5 107.05 65.47 10,049 1302.3 6233 111,155 15,164 38,426 7.11 187.96 6.79 35.23 23 6.18 24.25

1 56 1545 132 0.25 16 12 1573 52,000 25.5 59 123 0.2 2.5 103 10,800 1400 106 53.5 7900 924 5300 89,200 12,900 37,600 7 166 7 33 17.5 7 18

Fig. 6. Spatial distribution of grid squares with scores for factor 2 ‘‘Metallization” higher than percentile 90.

where there has been no significant mining activity. Other maxima for Mn (exceeding 3000 mg kg 1) are present in

areas where mining and metallurgical activities occurred, such as grid squares 11, 23 and 51 (Fig. 5). Table 6 shows

J. Martínez López et al. / Applied Geochemistry 23 (2008) 2324–2336

the mean values for each variable in the granite outcrops. These values were obtained from the grid squares grouping in cluster 4/9 (Tables 3 and 4) and they are assumed to be local background values for this lithology. These background values are 35 mg kg 1 Cu, 1212 mg kg 1 Pb, 100 mg kg 1 Zn, 1183 mg kg 1 Mn and 18 mg kg 1 for As. Median, standard deviation and median absolute deviation (MAD) values are also included in Table 6. The median plus twice the standard deviation represents the threshold value for each variable. It is considered that the use of the means and the standard deviations gives a better estimate of location even for skewed populations. Nevertheless, as the mean value it is very strongly influenced by high data outliers, the median plus twice median absolute deviation (MAD) has been used as it is statistically robust against a large number of outliers (Reimann and Filzmoser, 2000; Reimann and Garret, 2005). Considering this approach, 68 grid squares have values for the studied elements which are higher than the median plus 2MAD. Fig. 6 shows the location of grid squares 9, 11, 22, 23, 25, 33, 35, 37, 38, 49, 51, 53, 63, 91, with scores for factor 2 ‘‘metallization” higher than percentile 90. The geographic situation of these grid squares provides further indication of priority zones for future action. 4. Conclusions By means of statistical techniques, a qualitative and quantitative evaluation of the data obtained from intensive sampling of the soil in the study area has been carried out. Factor analysis enables identification of 4 factors that together account for 80% of the total variance observed. Factor 1 (termed ‘‘natural 1”) allows characterising granitic soils and distinguishing them from the other lithologies. Factor 2 (termed ‘‘metallization”) characterises both metallization and the existence of mining, metallurgical and mineral processing activities and it also establishes an order of priority depending on the overall degree of contamination of each of the grid squares caused by mining activity in the zone. Factor 3 (termed ‘‘natural 2”) enables distinguishing the Miocene (with high values of Ca, Mg and Sr) from the Triassic soils. Finally, factor 4 (termed ‘‘fuels”) enables to characterisation of the processes of fossil fuel combustion. According to the reference levels established by the Regional Government (Junta de Andalucía), soils in a large part of the study area require amendment applications, 81 out of the 126 grid squares analyzed, if an agricultural use is considered. This study also reveals that 68 grid squares have concentrations (for one of more of five studied elements) higher than the median plus 2 MAD, indicating a significant level of effect. Considering a more restrictive contamination reference level, 14 grid squares that have values higher than percentile 90 for factor 2 ‘‘metallization” have been identified and they are selected as priority zones for remediation. The 126 grid squares of the study zone have been classified on the basis of the type of activity carried out (mining, agricultural or urban) and on possible combinations of these (using Observation Cluster Analysis). Furthermore, the grid squares are differentiated according to the more specific character of the activity in question (extraction,

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processing – mineral processing or metallurgy –, waste tips, dehesa, olive groves, cereal cultivation and urban). It is even possible to distinguish between the different types of lithology (granitic colluvium, granite, Triassic, Miocene). Analysis of the different clusters revealed that the activity having greatest impact on the soil is that of metallurgy followed by mineral processing and that the impact of mining activity is perceptible even in zones where the mining was not as intensive. References Azcárate, J.E., 1977. Mapa geológico y memoria explicativa de la hoja 905 (Linares), escala 1:50.000. Instituto Geológico y Minero de España. Azcárate, J.E., Argüelles, A., 1971. Evolución tectónica y estructuras filonianas en el distrito minero de Linares. Congreso Hispano-LusoAmericano de Geología Económica, Madrid, Tomo I, Sección 4, pp. 17– 32. Chen, T.B., Zheng, Y.M., Lei, M., Huang, Z.Ch., Wu, H.T., Chen, H., Fan, K.K., Yu, K., Wu, X., Tian, Q.Z., 2005. Assessment of heavy metal pollution in surface soils of urban parks in Beijing, China. Chemosphere 60, 542– 551. De Miguel, E., Jiménez de Grado, M., Llamas, J.F., Martín-Dorado, A., Mazadiego, L.F., 1998. The overlooked contribution of compost application to the trace element load in the urban soil of Madrid (Spain). Atmos. Environ. 31, 2733–2740. De Miguel, E., Llamas, J.F., Chacón, E., Berg, T., Larssen, S., Roíste, O., Vadset, M., 1997. Origin and patterns of distribution of trace elements in street dust: unleaded petrol and urban lead. Atmos. Environ. 21, 2733–2740. De Miguel, E., Llamas, J.F., Chacón, E., Mazadiego, L.F., 1999. Sources and pathways of trace elements in urban environments: a multielemental quantitative approach. Sci. Total Environ. 235, 355–357. Einax, J.W., Soldt, U., 1998. Geostatistical and multivariate statistical methods for the assessment of polluted soils – merits and limitations. Chemom Intell Lab. Syst. 46, 79–91. Facchinelli, A., Sacchi, E., Mallen, L., 2001. Multivariate statistical and GISbased approach to identify heavy metal sources in soils. Environ. Pollut. 114, 313–324. Ferreira-Baptista, L., De Miguel, E., 2005. Geochemistry and risk assessment of street dust in Luanda, Angola: A tropical urban environment. Atmos. Environ. 39, 4501–4512. Fontboté, J.M., 1982. Mapa geológico y memoria explicativa de la hoja 70 (Linares), escala 1:200.000. Instituto Geológico y Minero de España. Gallego, J.L.R., Ordóñez, A., Loredo, J., 2002. Investigation of trace element sources from an industrialized area (Avilés, northern Spain) using multivariate statistical methods. Environ. Int. 27, 589–596. García, R., Maiz, I., Millán, E., 1996. Heavy metal contamination analysis of roadsoils and grasses from Guipúzkoa (Spain). Environ. Technol. 17, 763–770. Gutiérrez-Guzmán, J., 1999. Las minas de Linares. Apuntes históricos. Colegio Oficial de Ingenieros Técnicos de Minas de Linares, Linares. Huang, W., Campredon, R., Abrao, J.J., Bernat, M., Latouche, C., 1994. Variation of heavy metals in recent sediments from Piratininga Lagoon (Brazil): interpretation of geochemical data with the aid of multivariate analysis. Environ. Geol. 23, 241–247. Illescas, J.A., Llamas, J., Higueras, P., Del Barrio, S., 1996. Caracterización geoquímica de partículas sedimentadas en una población urbana e industrial: el caso de Puertollano (Ciudad Real). Geogaceta, 1152– 1154. Kalogeropulos, N., Karayannis, M.I., Vassililakigrimani, M., Grimanis, A.P., 1994. Application of trace elements determination and multivariate statistics to a pollution study of lake Pamvotis, NW Greece. Fres. Environ. Bull. 3, 187–192. Lillo, J., 1992. Geology and Geochemistry of Linares-La Carolina Pb-Ore field (Southeastern border of the Hesperian Massif). Ph.D. Thesis, Univ. Leeds. Lillo, J., 2003. Hydrothermal alteration in the Linares-La Carolina Ba–Pb– Zn–Cu–(Ag) vein district, Spain: mineralogical data from El Cobre vein. Trans. Inst. Min. Metal. Sect. B 111, B114–B118. Llamas, J.M., Hervás, L., Martínez Escriche, F., Otero, F., 2000. Suelos Contaminados. Revista Medio Ambiente 34. . Martínez, J., 2002. Caracterización geoquímica y ambiental de los suelos en el sector minero de Linares. Ph.D. Thesis, Univ. Politécnica de Madrid.

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