Metals in residential soils and cumulative risk assessment in Yaqui and Mayo agricultural valleys, northern Mexico

Science of the Total Environment 433 (2012) 472–481

Contents lists available at SciVerse ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Metals in residential soils and cumulative risk assessment in Yaqui and Mayo agricultural valleys, northern Mexico Maria M. Meza-Montenegro a, A. Jay Gandolfi b, María Ernestina Santana-Alcántar a, Walter T. Klimecki b, María Guadalupe Aguilar-Apodaca a, Rafael Del Río-Salas c, Margarita De la O-Villanueva d, Agustín Gómez-Alvarez e, Héctor Mendivil-Quijada c, Martín Valencia c, Diana Meza-Figueroa d,⁎ a

Departamento de Recursos Naturales, Instituto Tecnológico de Sonora, 5 de Febrero 818 Sur, 85000, Cd. Obregón, Sonora, Mexico Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ 85724‐0207, USA c Estación Regional del Noroeste, Instituto de Geología, Universidad Nacional Autónoma de México, Colosio y Madrid, s/n, 83000, Hermosillo, Sonora, Mexico d Department of Geology, University of Sonora, Rosales y Encinas 83000 Hermosillo, Sonora, Mexico e Departamento de Ingeniería Química, División de Ingeniería, Universidad de Sonora, Rosales y Encinas 83000 Hermosillo, Sonora, Mexico b

H I G H L I G H T S ► ► ► ► ►

Soil metal pollution associated with the Green Revolution in Mexico is studied. High levels of V, Cr, Ni, Mn, and Co are spatially associated to power plants. P, S, and Se are spatially associated to agricultural areas. Risk assessment shows a probability of adverse health effects. Metals are less enriched in dust fraction when compared to bulk soils

a r t i c l e

i n f o

Article history: Received 6 February 2012 Received in revised form 20 June 2012 Accepted 21 June 2012 Available online 21 July 2012 Keywords: Dust Metal Risk assessment Soils

a b s t r a c t This investigation examines the extent of soil metal pollution associated with the Green Revolution, relative to agricultural activities and associated risks to health in the most important agricultural region of Mexico. Metal contents in bulk soil samples are commonly used to assess contamination, and metal accumulations in soils are usually assumed to increase with decreasing particle size. This study profiled the spatial distribution of metals (Ni, Cr, Pb, Cu, Fe, Cd, V, Hg, Co, P, Se, and Mn) in bulk soil and fine-grained fractions (soil-derived dust) from 22 towns and cities. The contamination of soil was assessed through the use of a geoaccumulation index (Igeo) and pollution index (PI). The results of this study indicated that a number of towns and cities are moderately to highly polluted by soil containing Be, Co, Hg, P, S, V, Zn, Se, Cr, and Pb in both size fractions (coarse and fine). Hazard index in fine fraction (HIchildren = 2.1) shows that risk assessment based on Co, Mn, V, and Ni spatially related to power plants, have the potential to pose health risks to local residents, especially children. This study shows that risk assessment based on metal content in bulk soil could be overestimated when compared to fine-grained fraction. Our results provide important information that could be valuable in establishing risk assessment associated with residential soils within agricultural areas, where children can ingest and inhale dust. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The potential public health risk associated with the intake of metals from dust and soil has been the subject of discussion in recent years (Wei and Yang, 2010). In urban areas, heavy metals in urban

⁎ Corresponding author at: Department of Geology, University of Sonora, Rosales y Encinas s/n 83000, Hermosillo, Sonora, Mexico. Tel.: +52 662 2592110; fax: +52 662 592111. E-mail address: [email protected] (D. Meza-Figueroa). 0048-9697/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.scitotenv.2012.06.083

soils and dusts can be accumulated in the human body via direct inhalation, ingestion, and dermal contact absorption (De Miguel et al., 1998; Madrid et al., 2002; Marjorie et al., 2008). In agricultural areas, the human predominant exposure to metals is via the soil–crop system (Liu et al., 2007). The anthropogenic sources of metals in urban areas include traffic emission (vehicle exhaust particles, tire wear particles, brake lining wear particles), industrial and domestic emission, and atmospheric deposition (Morton-Bermea et al., 2009; Sezgin et al., 2003). Anthropogenic sources of metals in agricultural areas include mining, waste disposal, sewage, pesticides, fertilizers, and vehicle exhausts (Li et al., 2008; Montagne et al., 2007).

M.M. Meza-Montenegro et al. / Science of the Total Environment 433 (2012) 472–481

473

United States of America

Sonora

N

México

Hermosillo Gulf of California

100 km

600000

560000

680000

640000

3080000

Vicam Potam Bacum San José Bácum

Cocorit Providencia

Campo 60 San Ignacio

Campo 47

Yaqui valley

Esperanza

3040000

Obregon City

Campo 5 Tobarito Quetchehueca Pueblo Mayo

Colonia Allende

Bácame

N

Navojoa city

Villa Juárez Buaysiacobe Basconcobe

Gulf of California 20 km

Bacobampo

Mayo valley Etchojoa

Huatabampo

2960000

Fig. 1. Location of the study areas (Yaqui and Mayo valleys) and distribution of sampling points.

Elevated levels of metals in residential soils may pose risks to human health. The risk is especially high for children exposed to Pb, As, Cd, Cr, and Hg, because of their low tolerance to pollutants as well as the inadvertent ingestion of dusts or soils, through handto-mouth pathways (Acosta et al., 2009). Metal contamination in soil is commonly assessed by comparison against soil quality standards and regulations. Studies had shown that the concentration of metals in soils increases with decreasing particle size (Wei and Yang, 2010; Ljung et al., 2006; Wang et al., 2006). Smaller particles are of concern since they are more efficiently adsorbed (e.g. inhalation) than coarse fractions (Lin et al., 1998), and the fine-grained fraction of soil is easily suspended into the atmosphere by wind erosion. Stanek et al. (1999) suggested that risk assessment linked to dust ingestion is more reliable if based on fine particle size instead of bulk soil. The Yaqui and Mayo valleys are the most important agricultural areas in Mexico. The valleys are located in southern Sonora, a border state with the United States of America (Fig. 1). The Green Revolution began in the Yaqui valley between the 1940s and the late 1970s. The

valleys are situated on a coastal strip along the Gulf of California, and consist of an intensively managed agricultural region amidst a desert scrub forest bordered by estuarine ecosystems that provide critical habitat for migratory and resident water birds, marine mammals, fish, and shellfish populations (Flores-Verdugo et al., 1992). Both valleys host 566,000 ha of irrigated wheat-based agriculture and the development of the region is of vital economic importance to Mexico because it produces some of the highest wheat yields in the world (FAO, 1997). The Yaqui and Mayo valleys are undergoing rapid socioeconomic and ecological changes due to the population growth, urbanization, agricultural intensification, and coastal aquaculture development. González et al. (1997) reported 399 industrial facilities in the Yaqui valley (247 small, 96 medium, 56 large). Obregon city is the second largest city in Sonora and contains most of the large industries. Many of these industrial plants produce refuse and sewage, which may affect the aquifer since this waste flows into the Gulf of California (González et al., 1997). Farming activities in the Yaqui valley generate a pollution load including manure and urine, amounting to around

474

M.M. Meza-Montenegro et al. / Science of the Total Environment 433 (2012) 472–481

Table 1 Metal content in soils from agricultural Yaqui valley. Maximum permissible levels for metals in Mexican legislation are: Ba (5400), Cd (37), Hg (23), Ni (1600), Ag (390), Pb (400), Se (390), Tl (5.2), and V (78) (NOM-147-SEMARNAT/SSA1-2004). All concentrations are expressed in mg kg−1 unless otherwise stated. Metals

Cd. Obregón/ Esperanza (n = 16)

Bácum (n = 10)

San José de Bácum/ Campo 60 (n = 19)

San Ignacio (n = 10)

Campo 47 (n = 10)

Pueblo Yaqui (n = 10)

Quetchehueca Providencia (n = 30) (n = 10)

Campo 5 (n = 10)

Pòtam (n = 1)

Vìcam (n = 1)

Ti

5018–3043

5744–3749

6060–3870

5360–3886

5138–3759

5901–3736

4904–1920

4915–3580

4860–3608

5465

6978

4031 776.4 152–25

4747 551 125–25

4965 491 145–25

4623 595 142–25

4449 446 95–25

4819 677 101–25

3412 687 135–25

4248 423 125–25

4234 421 128–25

– – 25

– – 99

89 45 620–238

75 48 722–544

85 45 738–443

84 47 624–493

60 31 729–536

63 30 724–454

80 39 680–256

75 44 578–338

77 48 649–434

– – 596

– – 646

429 89 26,389– 11,056 18,723 3912 60–45

633 50 28,645– 23,763 26,204 1799 75–45

591 90 30,637– 21,105 25,871 2516 76–45

559 45 28,617– 21,937 25,277 1976 77–44

633 71 27,422– 23,055 25,239 1388 73–45

589 87 27,853– 18,506 4674 3049 72–45

468 115 24,773– 10,165 17,469 3800 73–45

458 70 24,710– 18,892 21,801 1883 82–45

542 66 30,774– 19,382 25,078 3078 56–45

– – – – 28,216 31,470 – – 45

– – 71

53 7 50–43

60 11 73–29

61 10 55–43

61 11 43–42

59 13 58–28

59 10 48–34

59 10 87–43

64 12 63–43

51 7 50–43

– – 43

– – 40

47 6 165–53

51 14 143–72

49 7 123–70

43 5 93–58

43 10 121–61

41 5 146–77

65 12 360–37

53 11 337–63

47 7 417–62

– – 82

– – 59

108 31 113–80

108 24 109–63

97 18 108–93

76 10 110–93

91 20 122–69

112 22 105–82

199 82 107–76

200 81 107–88

240 107 114–91

– – 101

– – 59

97 10 720–345

86 13 596–502

101 5 614–489

102 5 575–500

96 14 773–401

94 8 569–414

92 8 670–369

98 7 638–416

103 7 637–428

– – 557

– – 562

533 87 84–37

549 28 97–6

552 37 125–36

538 24 70–38

587 96 78–6

492 63 145–34

520 71 96–6

527 74 65–26

533 57 81–27

– – 60

– – 75

61 13 605–330

52 27 704–281

81 20 744–379

54 10 682–366

42 21 653–446

90 31 639–261

51 24 630–314

46 13 688–371

54 15 676–395

– – 522

– – 588

468 97 27–17

493 130 57–11

562 101 55–16

524 95 25–14

550 62 37–11

450 101 54–16

472 74 82–10

530 105 58–15

536 83 195–18

– – 22

– – 13

22 4

34 14

36 10

20 4

24 9

35 11

46 15

37 15

107 54

– –

– –

Max– min Mean SD Cr Max– min Mean SD Mn Max– min Mean SD Fe Max– min Mean SD Ni Max– min Mean SD Cu Max– min Mean SD Zn Max– min Mean SD Rb Max– min Mean SD Sr Max– min Mean SD Zr Max– min Mean SD Ba Max– min Mean SD Pb Max– min Mean SD

3200 m 3 day −1 for pork, 850 m 3 day −1 for beef, and 650 m 3 day −1 from poultry (González and Córdova, 1992). Most of this waste is applied to the ground and the rest is drained into the sewer (González et al., 1997). According to González and Córdova (1992) about 233 m3 day−1 of fertilizer is used in the Yaqui valley. Thus there are many sources of environmental contamination in the Yaqui and Mayo Valleys. Metals are associated with many of the wastes produced in the Yaqui and Mayo Valleys, however only limited attention has been given to assessing these metal contaminants. Most studies from the Yaqui valley are focused on arsenic-exposure health effects (Adler, 2005; Cantú-Soto et al., 2009; Meza et al., 2004, 2005). Epidemiological studies performed at the Yaqui valley focused in the effects of arsenic exposure on nucleotide excision and provide evidence that supports the ability of arsenic to inhibit the DNA repair machinery, which is likely to enhance the genotoxicity and mutagenicity of other genotoxic compounds as part of a cocarcinogenic mechanism

of action (Andrew et al., 2006). Guillete et al. (2006) conducted a study of breast development in a group of peripubertal girls from the Yaqui valley (pesticide exposure) and Yaqui foothills (non pesticide). The study shows that girls from valley towns displayed a poorly defined relationship between breast size and mammary gland development, whereas girls from the Yaqui foothills, where traditional ranching occurs, show a robust positive relationship between breast size and mammary size. Such differences were attributed to environmental influence, however, arsenic exposure through water consumption has been explored as the only exposure path in the Yaqui valley. The main objective of this study was to determine the distribution of metals in bulk soils and fine-grained fraction (soil-derived dust) and to estimate the degree of contamination on the basis of geoaccumulation and pollution indexes. Attention will be given to the potential health risk to adults and children via fine-grained

M.M. Meza-Montenegro et al. / Science of the Total Environment 433 (2012) 472–481

475

Table 2 Metal concentrations (mg kg−1) in soils from Mayo valley. Metals

Villa Juárez (n = 10)

Buaysiacobe (n = 10)

Bacobampo (n = 10)

Basconcobe (n = 10)

Etchojoa (n = 10)

Huatabampo (n = 10)

Pueblo Mayo (n = 10)

Navojoa (n = 10)

Bácame (n = 10)

Ti

3913–2260 3087 539 107–25 66 33 521–206 364 100 22,620–10,024 16,322 4140 56–45 51 5 43–41 42 1 92–38 65 18 106–84 95 7 538–377 458 53 89–6 48 27 586–306 446 72 30–13 22 5

4728–3039 3884 476 115–25 70 34 626–370 498 82 24,588–16,459 20,524 2845 64–45 55 9 43–38 41 3 111–59 85 18 94–79 87 5 605–468 537 53 97–20 59 25 575–404 490 55 25–12 19 4

5868–2716 4292 957 129–25 77 45 750–380 565 117 29,618–14,615 22,117 5187 72–37 55 12 43–42 43 0.1 113–60 87 16 89–74 82 4 598–453 526 45 93–6 50 24 664–370 517 102 24–12 18 5

5205–2986 4096 651 100–25 63 35 709–392 551 99 29,622–17,502 23,562 3522 80–42 61 12 44–43 43.5 0.5 172–66 119 32 90–77 84 5 588–496 542 33 81–39 60 14 640–428 534 82 27–9 18 6

5052–2719 3886 723 136–25 81 48 844–422 633 124 29,982–16,739 23,361 4288 65–45 55 9 43–38 41 3 108–37 73 19 102–75 89 8 567–391 479 50 67–39 53 10 693–403 548 94 27–14 21 4

4510–2159 3335 697 100–25 63 33 713–331 522 122 28,459–13,212 20,836 4772 69–35 52 10 41–23 32 5 107–51 79 17 106–74 90 10 545–400 473 42 75–18 47 21 724–452 588 82 103–9 56 27

4981–1648 3315 949 113–25 69 36 590–227 409 113 25,844–10,070 17,957 4642 62–45 54 8 43–39 41 2 64–31 48 11 109–78 94 9 577–266 422 96 111–6 59 38 613–393 503 69 28–10 15 6

4881–1648 3723 672 158–25 92 51 781–263 522 135 31,821–12,404 22,113 5034 78–31 55 15 43–39 41 2 123–44 84 29 115–61 88 16 732–230 481 137 137–6 72 32 620–372 496 83 33–13 23 8

4237–1756 2997 837 91–25 58 21 465–209 337 79 18,241–7198 12,720 3540 73–45 59 13 46–43 44.5 1 182–30 106 44 116–81 99 10 516–322 419 72 128–21 75 33 492–274 383 66 24–10 17 6

Cr

Mn

Fe

Ni

Cu

Zn

Rb

Sr

Zr

Ba

Pb

Max–min Mean SD Max–min Mean SD Max–min Mean SD Max–min Mean SD Max–min Mean SD Max–min Mean SD Max–min Mean SD Max–min Mean SD Max–min Mean SD Max–min Mean SD Max–min Mean SD Max–min Mean SD

fraction and bulk soil ingestion. To our knowledge, this is the first study that characterizes the extent of soil pollution caused by the Green Revolution agricultural activities and associated risks to health in the Yaqui and Mayo valleys. 2. Methods and materials 2.1. Sample collection A total of 197 surface soil samples were collected from 20 towns (rural) and 20 soil samples from two cities (urban). In each location a random selection of ten sampling sites within the community was made locating their position using a geographic position system (Fig. 1). Residential soil samples were collected to a depth of 10 cm. Sieved soil samples of −325 mesh were obtained and analyzed. The fine-grained fraction represents dust of 44 μm, which is the most likely re-suspended soil fraction by wind and anthropogenic activities (Laidlaw and Filippelli, 2008). Five composite samples of fine-grained soil fraction come from the Yaqui valley, and three composite samples from the Mayo valley. Each composite sample was obtained from ten samples from each locality. 2.2. Sample analysis Bulk soil samples were analyzed for metal (Ti, Cr, Mn, Fe, Ni, Cu, Zn, Rb, Sr, Ba, Pb) content after sieving using an Innov-XT400 portable X-ray fluorescence (XRF) analyzer with a miniature, rugged, X-ray tube excitation source. The XT400 XRF analyzer uses a Hewlett– Packard (HP) iPAQ personal data assistant for data storage. The certified standard NIST SRM-2702 (Inorganics in marine sediments) was

also analyzed by the XT400 with recoveries ranging from 90% to 110%. The detection limits expressed in mg kg −1 were as follows: Ti (400), Cr (45), Mn (80), Fe (100), Ni (70), Cu (50), Zn (30), Rb (11), Sr (13), Zr (10), Ba (520), Hg (14), and Pb (16). In order to verify the XRF analysis, 10% of the samples were analyzed using acid digestion coupled with plasma atomic emission spectrometry (ICP-AES), in accordance with USEPA Method 3050B/6010B. ICP-AES analyses were performed using a Perkin-Elmer 4200 DV coupled with a hydride generation device at the University of Sonora. When compared, the XRF results were within 10% agreement with the ICP-AES results. Residential soil samples were analyzed by this method. Fine-grained soil fraction (dust) samples were analyzed by ICP-MS method MS45 at ALS-CHEMEX laboratories (Vancouver, Canada). 2.3. Statistical analysis Descriptive statistical parameters such as minimum, maximum, mean and standard deviation were estimated for the metal content in studied samples. Multielemental correlation matrix and their corresponding scatter plots were done using Geosoft's Geochemistry 7.2 software. To calculate the correlation strength, a percentile of 95% was used (as 0.95). 2.4. Spatial mapping All interpolation images and maps were done using ESRI's ArcMap 10 and Geosoft's Target for ArcGIS 3.5. The statistical method used for data interpolation was Ordinary Kriging with no anisotropy values applied, since the soil distribution in the area is fairly continuous.

476

M.M. Meza-Montenegro et al. / Science of the Total Environment 433 (2012) 472–481

Table 3 Geochemical data of soil-derived dust samples from the Yaqui and Mayo valleys. Concentrations are in mg kg−1, unless otherwise stated. Element mg kg−1

Yaqui valley Vicam rural

Potam rural

Bacum rural

Cocorit rural

Campo 5 rural

Obregon urban

Etchojoa rural

Navojoa urban

Bacobampo rural

Cu Pb Zn B Li Sb Mo Ni Ti % S% Hg Re V Mn Sr Al % Sc Ca % K% Rb Y Co Se Ga Ge Na % Th La Ba Cd Fe % P Mg % Cr U Zr

30.5 11.4 77 10 14.3 0.46 1.16 26.4 0.14 0.12 0.04 0.001 119 817 309 2.3 10.8 2.8 0.49 17.9 24.9 21.3 0.7 7.46 0.12 0.37 5.7 25.3 730 0.17 4.31 1110 0.97 38 0.99 26.9

21.8 18.3 77 10 27.5 1.39 0.78 17.6 0.12 0.30 0.06 0.001 88 519 206 1.57 5.4 1.53 0.28 18.2 14.6 13.3 0.4 5.36 0.1 0.69 7 22.1 220 0.18 3.42 1530 0.86 26 1.65 15.1

31.5 22 108 10 27.4 1.52 1.3 18.8 0.10 0.20 0.04 0.001 79 663 231 1.69 4.8 2.1 0.52 16.2 15.2 13.9 0.6 5.59 0.14 0.22 5.1 23.4 230 0.23 3.28 2210 0.97 24 1.17 10.2

29.1 24.5 120 10 26.5 1.52 1.19 17.7 0.11 0.21 0.07 0.002 81 559 243 1.58 4.6 2.5 0.41 17.3 13.95 13 0.5 5.32 0.12 0.3 5.1 22 220 0.22 3.16 1890 0.91 25 1.22 9.4

24.2 23.2 89 10 18.5 1.26 0.57 15.7 0.05 0.04 0.04 b0.001 57 547 226 1.97 5 1.3 0.53 17.4 13.5 11.5 0.5 5.94 0.1 0.07 4.7 20.9 240 0.26 2.64 1470 0.67 21 0.98 6.6

27.7 26.2 133 10 23.7 1.26 1.05 19.5 0.07 0.15 0.16 0.001 81 622 308 2.04 5.1 3.2 0.39 3.27 14.1 13.7 0.5 6.23 0.11 0.19 5.9 22.1 350 0.35 2.98 1660 0.85 27 1.24 7.9

25.4 19.8 96 10 30.1 1.19 1.06 19.8 0.12 0.22 0.15 0.001 83 669 250 2.09 6.3 1.6 0.41 2.66 15.05 15 0.7 7.07 0.12 0.25 4.6 22.2 270 0.29 3.31 1410 0.98 26 1.06 12.8

22.7 20.5 98 b10 22.8 1.22 0.6 16.8 0.07 0.06 0.04 b0.001 71 583 265 2.07 5.5 2.6 0.31 14.8 12.85 12.6 0.4 6.38 0.09 0.12 5.8 20.9 290 0.24 2.75 1030 0.89 25 1.12 8.6

22.8 18.5 88 b10 0.83 5.1 0.16 16.6 0.10 0.10 0.13 b0.001 72 591 249 1.84 5.1 1.8 0.37 18.9 12.7 12.6 0.6 5.93 0.12 0.16 4.8 18.9 250 0.3 2.87 1400 0.83 23 1.09 8.9

Mayo valley

2.5. Geoaccumulation index (Igeo) and integrated pollution index (IPI) The widely used index of geoaccumulation (Igeo) was employed to assess the contamination of soils (Yaqin et al., 2008), and determine contamination by comparing current metal contents with preindustrial levels. For this method, the content accepted as background is multiplied each time by the constant 1.5 in order to take into account natural fluctuations of a given substance in the environment as well as a very small anthropogenic influence. The value of the geoaccumulation

Table 4 Metal concentrations in agricultural topsoils. Abbreviations are: nr, not reported. All concentrations, but Fe, are expressed in mg kg−1. Abbreviations are: not reported (nr). Location Reference

Ti

Cr

Mn

Fe wt%

Ni

Cu

Zn

Pb

China Iran

403 nr

70 68

275 nr

4.3 nr

nr 37

33 36

81 218

39 17

nr

20– 30 40

50

nr

70– nr 0.7– 50 100 4.2 125 1667 nr 158

477

10– 30 81

nr nr

28 nr

nr

12– nr 75

24 0.7– 269 17– 46

22 0.3– 495 8– 46

58 1.5– 264 61– 401

20 0.5– 135 12– 75

45– 82

23– 87

30– 417

10– 56

Italy Turkey Spain United States Serbia

Sonora

Teng et al. (2010) Hani and Pazira (2011) Abolino et al. (2002) Aydinalp and Marinova (2003) Micó et al. (2006) Holmgren et al. (1992) Škrbic and Ðurišic-Mladenovic (2010) This study

index is described by the following equation: Igeo = log2 {Cn /[1.5× Bn]} where Cn is the metal content in tested soil, Bn is the background content of the studied metal, which is taken from the reference values of the world average of soils (Bowen, 1979). The interpretation of the obtained results is as follows: if Igeo ≤0, practically uncontaminated; 0b Igeo b 1, uncontaminated to moderately contaminated; 1b Igeo b 2, moderately contaminated; 2b Igeo b 3, moderately to heavily contaminated; 3b Igeo b 4, heavily contaminated; 4b Igeo b 5, heavily to very heavily contaminated; and Igeo ≥5, very heavily contaminated. The pollution level of potentially harmful metals was estimated by integrated pollution index (IPI, Chen et al., 2005; Wei et al., 2009). The IPI is defined as the mean value of the pollution index (PI) of an element. In this study, the PI of each element is defined as the ratio of metal concentration in the studied areas to the background concentration of metals as the following equation: PIi = Ci / Bi. Where Ci is the concentration of metal in environment, Bi is the background value taken from world average for soils. The IPI is classified as: if IPI ≤ 1, low level of pollution; 1 b IPI ≤ 2, moderate level of pollution; 2 b IPI ≤ 5, high level of pollution; and IPI > 5, extreme high level of pollution. 2.6. Risk assessment

320 nr

1.5 nr nr

1648– 25– 206– 1– 6978 158 844 3.2

Risk assessment with regard to exposure to metal-contaminated soils by ingestion was carried out to estimate the noncancer toxic (chronic) risk of dust-exposed population in Yaqui and Mayo valleys. Estimation of risk was calculated based on equations detailed in USEPA's Risk Assessment Guidance for Superfund (RAGS) Part A. Average daily dose (ADD) was determined by the following equation:

M.M. Meza-Montenegro et al. / Science of the Total Environment 433 (2012) 472–481

Ni

477

Pb

Cr

Cu Fig. 2. Spatial distribution maps of Ni, Cr, Pb, and Cu concentration in soils.

ADD = [C × IngR × EF × ED] ⁄ [BW × AT]. Where C is the mean metal concentration (mg kg −1) in fine-grained soil fraction (or soilderived dust). Conservative estimates of dust ingestion rates, IngR, were chosen for adults (100 mg day−1) and children (200 mg day−1). An average body weight, BW, of 60 kg for adults and 16 kg for children was considered. Exposure frequency, EF = 350 days year − 1; exposure duration, ED = 6 years; and the averaging time, AT = 2190 days. Noncancer toxic risk was determined by calculating the hazard quotient, HQ, where HQ = ADD / RfD and RfD is an estimate of the daily exposure to the human population (including sensitive groups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Therefore, HQ ≤ 1 suggests unlikely adverse health effects, whereas HQ > 1 suggests the probability of adverse health effects (U.S. EPA, 1993). An HQ > 10 is considered to be high chronic risk. HQs can be added and can generate a Hazard Index (HI) to estimate the risk of mixing contaminants, therefore the HQ for each metal at a location was summed to generate the hazard index (HI). 3. Results 3.1. Soils The concentrations of Ti, Cr, Mn, Fe, Ni, Cu, Zn, Rb, Sr, Ba, and Pb in residential soils from urban and agricultural areas are shown in Tables 1 and 2. The range of the concentrations was from 15 mg kg −1 for Pb to 417 mg kg−1 for Zn. According to the mean concentrations of all samples analyzed here, the metal abundance could be ordered as follows: Mn> Ba >Zn> Pb > Cr> Cu>Ni for the Yaqui valley area

and Mn> Ba >Zn> Cr> Ni> Pb > Cu for the Mayo valley area. Ba and Cr content had the highest variability in the samples while Cu and Ni levels were the least variable (Tables 1 and 2). The values for maximum permissible concentrations of potentially toxic elements for residential soils according to Mexican legislation are included in Table 1. When compliance with national limits for metals in soils is considered, none of the studied samples exceeded the limit values (Table 1). Comparison of the results with those found in the literature for the metals in agricultural top soils from China, Iran, Italy, Turkey, Spain, Serbia, and United States is shown in Table 4. The Mayo and Yaqui soils contained more Pb, Cu, Cr, and Zn than those from Italy, China, Iran, Serbia, and Spain. Cr, Pb and Zn concentrations for the Yaqui and Mayo soils exceeded maximum agricultural soil concentration in some European countries (KabataPendias and Pendias, 2001, not shown in Table 4). The distribution patterns of the metals were examined (Figs. 2–4). Urban areas (Obregon and Navojoa) are characterized by high Cr values in soils. Cu, Zn, and Pb are spatially related to the agricultural valleys (Figs. 2–4). Spatial distribution of Ni and Cr (Fig. 2) shows two impacted areas that are spatially related to agricultural fields and power plants. Spatial distribution of Mn, Fe, Ba, and Zn are mainly concentrated within the developed agricultural area of Yaqui valley (Fig. 3) while field of high concentration is shown within the agricultural area of Mayo valley. Igeo and PI from Table 5 show values ranging from 0.1 to 5.6 (very low to very high level of pollution). Fig. 4a shows correlation coefficients for metal content in soils from the agricultural areas (both valleys). Ba–Fe, Ba–Mn, Sr–Fe–Mn, and Zn–Cu–Pb are moderately correlated (correlation coefficient ranging from 0.67 to 0.75). Fig. 4b

478

M.M. Meza-Montenegro et al. / Science of the Total Environment 433 (2012) 472–481

Mn

Ba

Fe

Zn Fig. 3. Spatial distribution maps of Mn, Ba, Fe, and Zn concentration in soils.

shows a Pearson correlation matrix of metal concentrations in finegrained fractions, where V–Co–Cr–Fe–Ni is strongly correlated (correlation coefficient above 0.9). The IPIs of soil samples varied from 0.1 to 3.1. IPI value of 3.1 is reported for Pb in Campo 5, followed by Cr IPI value of 2.6 in Bacobampo. IPI values above 2 are reported for Pb and Zn in Campo 5 and Cu in Quetchehueca, indicative of a high level of pollution in soils. Middle level of pollution was obtained for Cu>Ni>Pb>Cr for all localities in both valleys. On the basis of IPI analysis, Ti, Mn, and Fe are considered uncontaminated and geogenic.

3.2. Fine-grained fraction of soil The concentrations of metals in fine-grained fractions (dust) are shown in Table 3. There are no guidelines in Mexican legislation for regulation of metals in dust, however, this study considered obtained sieved soil samples of − 325 mesh because they represent dust of 44 μm, which is the most likely re-suspended soil fraction. Obregon city shows the highest values for Pb, Hg, Zn, and Cd (Fig. 4). The highest values for Se, V, Co, Cr, Ba, Ni, Zn, and P were

Fig. 4. A) Yaqui-Mayo soil assays correlation. B) Yaqui-Mayo fine fraction assay correlation. Bold squares represent strong to very strongly correlated pair.

M.M. Meza-Montenegro et al. / Science of the Total Environment 433 (2012) 472–481 Table 5 Geoaccumulation and pollution indexes of metals in residential soils within the Yaqui and Mayo valleys in Sonora.

450 400 350

Yaqui Valley

Mayo Valley

Obregon Bacum San Jose de Bacum San Ignacio Campo 47 Pueblo Yaqui Quetchehueca Providencia Campo 5 Potam Vicam Villa Juarez Buaysiacobe Bacobampo Basconcobe Etchojoa Huatabampo Pueblo Mayo Bacame Navojoa

Cr

Ni

Cu

Zn

Pb

Igeo PI

Igeo PI

Igeo PI

Igeo PI

Igeo PI

300

0.5 0.3 0.5

2.2 0 1.8 0 2.1 0

1.2 0.2 1.5 0.7 1.5 0.3

1.7 0.3 2.4 0.1 1.8 0

1.8 0 1.6 0.1 1.4 0.1

0.8 1.6 1.6

0.4 0 0 0.4 0.3 0.3 0 0 0 0.1 0.3 0 0.4 0 0.1 0 0.6

2 1.4 1.4 1.9 1.8 1.8 0.4 1.4 1.5 1.6 1.8 1.4 1.9 1.4 1.6 1.3 2.3

1.5 1.5 1.4 1.5 1.6 1.1 0.9 1.4 1.1 1.1 1.4 1.6 1.3 1.4 1.2 1.5 1.6

1.4 1.9 1.6 2.9 2.1 1.7 1.4 1.3 1.4 1.3 1.4 1.5 1.3 1.4 1.3 1.5 2.3

1 1.3 1.6 4 3.7 4.6 0.9 0.7 1 1.2 1.3 1.9 1.2 1.2 0.7 2 1.4

0.7 1.1 1.5 2.3 1.7 5.6 0.6 0.4 0.7 0.7 0.7 0.8 0.8 2.9 0.8 0.5 0.9

0 0 0 0 0.1 0 0 0 0 0 0 0.1 0 0 0 0 0.1

0 0.4 0.1 0.9 0.5 0.2 0 0 0 0 0 0 0 0 0 0 0.6

0 0 0.1 1.4 1.3 1.6 0 0 0 0 0 0.3 0 0 0 0.4 0

0 0 0 0.7 0.1 1.9 0 1.3 0 0 0 0 0 1 0 0 0

found in Vicam (Yaqui valley) and Etchojoa (Mayo valley). High Hg values are also reported for Obregon, Etchojoa, and Bacobampo. IPI values show that As, Be, Co, Hg, P, and S are highly enriched in the fine fraction from all studied areas within both Yaqui and Mayo valleys (Fig. 5), particularly S and P which are most likely related to agricultural activities and they represent important elements to consider in health risk assessment for dust exposure paths. The average pollution levels of Cr, Ni, Cu, Zn and Pb in the Yaqui valley soils expressed in terms of geoaccumulation indexes (Igeo) indicate that the environment is uncontaminated to moderately polluted. Most Igeo values for the Yaqui valley are within the category of moderately polluted (Table 4). Mayo valley Igeo values indicate that the soils are uncontaminated to moderately polluted, with most Igeo values showing uncontaminated soils (0 values, Table 4). 3.3. Cumulative risk assessment: hazard quotient. Noncancer toxic risk Eleven potentially harmful metals (Cu, Ba, Li, Mn, Cr, Ni, Zn, V, Co, Se, and Hg) were considered for hazard index calculation. The calculated HQs and HIs for Cr, Ni, and Zn using maximum measured metals in dust (HQdust, HIdust) and soil (HQsoil, HIsoil) ingestion pathway for adults' and children's scenarios are shown in Fig. 6. A value of

IPIs 5 4.5 4 3.5 3 2.5

VICAM ETCHOJOA POTAM OBREGON NAVOJOA BACUM COCORIT CAMPO 5 BACOBAMPO

High level of pollution

2 1.5

Middle level of pollution

1 0.5

Low level of pollution

Al Nb Y B Ba K Ni Cr Cs Cu Fe Cd Li Mn Mo Na Pb V Sb Sc Sr Th U Ce Zn Se Mg Ca As Be Co Hg P S

0

Fig. 5. Integrated pollution index for fine-grained soil samples.

479

HI children =0.6

SOIL

HI children =0.2

FINE GRAINED

250 200 150 100 50 0 Cr bulk soil

Cr fine grained

Ni bulk soil

Ni fine grained

Zn bulk soil

Zn fine grained

Fig. 6. Cr, Ni and Fe contents in soil and fine-grained fraction. HI is hazard index for Cr, Ni and Fe.

HIchildren = 0.6 was obtained for soil and HIchildren = 0.2 was obtained for fine-grained fraction or dust. Such result is important because it is commonly assumed that metals are preferentially concentrated in fine-grained fractions of soils. Fig. 6 shows Cr, Ni, and Zn soilconcentrations higher than dust-concentrations. Most studied metals in this study are also enriched in bulk soil. HIs were calculated using the metal content in fine-grained fractions. Estimated HIs for Co, Pb, Cr, Ni, Zn, Mn, V, Ni were the highest (HIchildren = 2.1, HIadults = 0.3), and together contribute to the 98% of the accumulative risk (HIdust) in Obregon (urban), Vicam, Potam, Bacum, and Cocorit, all of these rural communities are located near the major highway. Overall, the accumulative risks due to the studied metals content in dust are a major concern in Vicam and Obregon. Vicam locality shows the highest HIdust because of the particularly high values of V and Ni. Within the Yaqui valley, the localities with the highest HIchildren and HIadult are Vicam > Obregon > Bacum >Cocorit.

4. Discussion 4.1. Spatial distribution The spatial distribution of metal concentrations is a useful aid to assess the possible sources of enrichment and to identify hot-spot areas with high metal concentration. High values of Pb, Hg, Zn, and Cd from Obregon soils are possibly related to urban sources such as traffic and industrial activities. High concentrations of P and Se were detected in areas with extensive agricultural activities. Spatial distribution of Pb in the agricultural areas may possibly be a consequence of past use of lead arsenate as a pesticide in the Yaqui valley since there is a lack of industrial sources or traffic within the rural area. Several studies have reported that intra-soil redistribution of Pb and As derived from lead arsenate sprays is limited and confined to the topsoil (Peryea, 1990; Peryea and Kammereck, 1997). Zn is a minor constituent of some fungicides that are applied to wheat and potatoes. The Yaqui valley, produces 40% of Mexico's annual wheat output (Lobell et al., 2002). Agrochemicals commonly used in the Yaqui valley include herbicides (34%), carbamates (25.7%), organophosphates (27.5%), fungicides, organochlorines and pyrethroids (Garcia de Llasera and Bernal-Gonzalez, 2001). Class dithiocarbamate fungicides are organic Zn and Mn complexes in which the trace metals are chelated to organic dithiocarbamate ligands (Phinney and Bruland, 2009). Spatial distribution of V, Cr, Mn, Ba, and Ni in soil shows a significantly higher concentration near the towns of Vicam and Etchojoa where power plants are located. V has been shown to be an important element for tracing the emissions of a power station (Boix et al., 2001; Querol et al., 1996). Along with Ni, V has been traditionally associated

480

M.M. Meza-Montenegro et al. / Science of the Total Environment 433 (2012) 472–481

to fuel combustion processes (Boix et al., 2001; Querol et al., 1996; Agrawal et al., 2010; Ganor et al., 1988; Young et al., 1993). Using Igeo and IPI calculations, the Yaqui valley soils are slightly more polluted than Mayo valley soils. These differences could be explained in terms of the length of time as agricultural sites, since agriculture was developed in the Yaqui valley before the Mayo valley. In addition, agricultural practices related to use of pesticides such as lead arsenates (now discontinued) could reflect an agrarian legacy of metals in soils from the Yaqui valley. 4.2. Health risk Contaminated soils can endanger human health. Exposure of humans to soil is through dust exposure that comprises inhalation and/or oral exposure (ingestion). Dermal exposure was not considered in this study since that risk is about 100 times smaller than the risk that arises from ingestion and inhalation. Studied communities within the Mayo valley show slightly lower HQdust values than the Yaqui valley. HI estimation based on metal content in dust fractions is more conservative than HI estimation based on metal content in bulk soils. These results are of importance because risk assessment based on bulk-soil geochemistry could be overestimated. In contrast to what is commonly assumed, our studies found that fine-grained soil fraction is not always enriched in metals when compared to bulk-soils (Fig. 6). Our results highlight the importance of study fine-grained fractions of soil when conducting a risk assessment, furthermore, future bioavailability studies simulating gastrointestinal fluids should be conducted in both dust and bulk soil. High values of V and Ni in studied samples from Vicam and Obregon (bulk soils and fine-grained fraction) significantly increased accumulative risk (HIchildren = 2.1, HIadults = 0.3). Skin and respiratory health problems have been related with Ni-exposure. Although oral toxicity of most nickel compounds is low, their ingestion may cause some gastrointestinal irritation (Nielsen, 1977). Common symptoms associated to V inhalation include coughing, bronchitis, and nose, eyes and throat irritation (Waters, 1977). Some children can ingest substantial amounts of soil on given days (Calabrese et al., 1997) and realistic estimates of soil pica are problematic. The U.S. Environmental Protection Agency (USEPA) has assumed that 95% of children ingest 200 mg soil day −1 or less for exposure assessment purposes, however, it has been documented that some children can ingest up to 60 g of soil during a single day; the frequency with which children experience acute poisoning from ingestion of contaminated soils is unknown. Calabrese et al. (1997) suggest that for some chemicals, including V, conservative soil criteria based on chronic exposure may not be protective of children during acute soil pica episodes. Further epidemiological studies involving exposure biomarkers should be conducted in this area in order to clarify the health effects of these metals. 5. Conclusions Spatial distribution of Ni–Cr–Mn and Fe in bulk soils as well as high correlation of V–Co–Cr–Fe–Ni in fine-grained fraction of soils, indicated that Vicam and to a lesser extent, Etchojoa are communities apparently impacted by power plant emissions. Further studies are needed for confirmation. Obregon and Navojoa soils show geochemical patterns of traffic/urban sources (Pb, Cr, Cd, Cu, Hg) mixed with agricultural (Zn, Cu, Pb, P, S, Se) and geogenic sources (Ti, Mn, Ba, Sr). Using Igeo calculations allowed us to conclude that the Yaqui valley is slightly more polluted than the Mayo valley in terms of metal content in bulk soils. IPI values above 2 are reported for P, S, Se, Zn, and Cu within agricultural rural areas. Middle level of pollution was obtained for Cu, Ni, Pb, and Cr for all studied sites. HIdust represent an important concern with values > 2.1 suggesting the probability of adverse health effects in children. Risk assessment based on bulk

soil is three times higher than risk assessment based on dust fraction. These results are opposite to what is commonly assumed in literature and highlight the importance of conducting more studies taking into account fine-grained fractions in risk assessment. Acknowledgments This research was supported by FOMIX-CONACYT-SONORA (No. SON-2005-C01-22879). Authors thank Michael Switala for sampling preparation.

References Abolino O, Aceto M, Malandrino M, Mentasti E, Sarzanini C, Petrella F. Heavy metals in agricultural soils from Piedmont, Italy. Distribution, speciation and chemometric data treatment. Chemosphere 2002;49:545–57. Acosta JA, Faz Cano A, Arocena JM, Debela F, Martínez-Martínez S. Distribution of metals in soil particle size fractions and its implication to risk assessment of playgrounds in Murcia City (Spain). Geoderma 2009;149:101–9. Adler T. The arsenic differential: metabolism varies between children and adults. Environ Health Perspect 2005;113(6):A404. Agrawal P, Mittal A, Prakash R, Kumar M, Singh TB, Tripathi SK. Assessment of contamination of soil due to heavy metals around coal fired thermal power plants at Singrauli region of India. Bull Environ Contam Toxicol 2010;85(2):219–23. Andrew AS, Burgess JL, Meza MM, Demidenko E, Wauch MG, Hamilton JW, Karagas MR. Arsenic exposure is associated with decreased DNA repair in vitro and in individuals exposed to drinking water arsenic. Environ Health Perspect 2006;114(8): 1193–8. Aydinalp C, Marinova S. Distribution and forms of heavy metals in some agricultural soils. Pol J Environ Stud 2003;12(5):629–33. Boix A, Jordan MM, Querol X, Santeliu T. Characterization of total suspended particles around a power station in an urban coastal area in eastern Spain. Environ Geol 2001;40:891–6. Bowen. Environmental chemistry of the elements. London: Academic Press; 1979. Calabrese EJ, Stanek EJ, James RC, Roberts SM. Soil ingestion: a concern for acute toxicity in children. Environ Health Perspect 1997;105(12):1354–8. Cantú-Soto EU, Harris R, Roberge J, Klimecki WT, Félix-Fuentes A, Meza-Montenegro MM. Determinación de polimorfismos del gen Arsénico 3 Metiltransferasa (As3MT) asociado al metabolismo del arsénico, en la población del valle del Yaqui, Sonora, México. Rev Latinoam Recurs Nat 2009;5(2):155–60. Chen T, Zheng Y, Lei M, Huang Z, Wu H, Chen H, Fan K, Yu K, Wu X, Tian Q. Assessment of heavy metal pollution in surface soils of urban parks in Beijing, China. Chemosphere 2005;60:542–51. De Miguel E, De Grado MJ, Llamas JF, Martín-Dorado A, Mazadiego LF. The overlooked contribution of compost application to the trace elements in playgrounds in Madrid (Spain). Sci Total Environ 1998;215:113–22. Flores-Verdugo F, Gonzalez-Fariaz F, Zaragoza-Araujo U. In: Seeliger U, editor. Mangrove ecosystems of the Pacific Coast of Mexico: Distribution, structure, litter fall, and detritus dynamics. Coastal plant communities of Latin AmericaAcademic Press Inc.; 1992. p. 269–88. Ganor E, Altshuller S, Foner HA, Brenner S, Gabbay J. Vanadium and nickel in dustfall as indicators of power plant pollution. Water Air Soil Pollut 1988;42(3–4):241–52. Garcia de Llasera MP, Bernal-Gonzalez M. Presence of carbamate pesticides in environmental waters from the northwest of Mexico: determination by liquid chromatography. Wat Res 2001;35(8):1933–40. González R, Córdova G. Evaluación del riesgo de contaminación de las aguas subterráneas del Valle del Yaqui, Sonora, México. Project report of Instituto Tecnológico de Sonora, Cd. Obregón, Sonora, México; 1992. 70 pp. González R, Marín LE, Córdova G. Hydrogeology and groundwater pollution of Yaqui Valley, Sonora, México. Geofís Int 1997;36(1):49–54. Guillete EA, Conard C, Lares F, Aguilar MG, McLachlan J, Guillette Jr LJ. Altered breast development in young girls from an agricultural environment. Environ Health Perspect 2006;114(3):471–5. Hani A, Pazira E. Heavy metals assessment and identification of their sources in agricultural soils of southern Tehran, Iran. Environ Monit Assess 2011;176:677–91. Holmgren GGS, Meyer MW, Chaney RL, Daniels RB. Cadmium, lead, zinc, copper, and nickel in agricultural soils of the United States of America. J Environ Qual 1992;22(2):335–48. Kabata-Pendias A, Pendias H. Trace elements in soils and plants. 3rd Edition. Boca Ratón, Florida: CRC Press; 2001. 413 pp. Laidlaw MAS, Filippelli GM. Resuspension of urban soils as a persistent source of lead poisoning in children: a review and new directions. Appl Geochem 2008;23: 2021–39. Li Y, Gou X, Wang G, Zhang Q, Su Q, Xiao G. Heavy metal contamination and source in arid agricultural soils in central Gansu province, China. J Environ Sci 2008;20: 607–12. Lin ZX, Harsbo K, Ahlgren M, Qvarfort U. The source and fate of Pb in contaminated soils at the urban area of Falun in Central Sweden. Sci Total Environ 1998;209: 47–58. Liu WX, Shen LF, Liu JW, Wang YW, Li SR. Uptake of toxic heavy metals by rice (Oryza sativa L.) cultivated in the agricultural soils near Zhengzhou City. People's Republic of China. Bull Environ Contam Toxicol 2007;79:209–13.

M.M. Meza-Montenegro et al. / Science of the Total Environment 433 (2012) 472–481 Ljung K, Selinus O, Otabbong E, Berglund M. Metal and arsenic distribution in soil particle sizes relevant to soil ingestion by children. Appl Geochem 2006;21:1613–24. Lobell DB, Asner GP, Ortiz-Monasterio JI, Benning TL. Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agric Ecosyst Environ 2002;1944:1-16. Madrid L, Díaz-Barrientos F, Madrid F. Distribution of heavy metal contents of urban soils in parks of Seville. Chemosphere 2002;49:1301–8. Marjorie C, Aeliona H, Davisa T, McDermottb S, Lawsonc AB. Metal concentrations in rural topsoil in South Carolina: potential for human health impact. Sci Total Environ 2008;402:149–56. Meza MM, Kopplin MJ, Burgess JL, Gandolfi AJ. Arsenic drinking water exposure among adults in the Yaqui Valley. Environ Res 2004;96(2):119–26. Meza MM, Yu L, Rodriguez YY, Guild M, Thompson D, Gandolfi AJ, Klimecki WT. Developmentally restricted genetic determinants of human arsenic metabolism: association between urinary methylated arsenic and CYT19 polymorphism in children. Environ Health Perspect 2005;113(6):775–81. Micó C, Peris M, Sánchez J, Recatalá L. Heavy metal content in agricultural soils in a Mediterranean semiarid zone: the Segura River Valley (Alicante, Spain). Span J Agric Res 2006;4(4):363–72. Montagne D, Cornu S, Bourennane HN, Baize D, Ratié C, King D. Effect of agricultural practices on trace-element distribution in soil. Commun Soil Sci Plant Anal 2007;38:473–91. Morton-Bermea O, Hernández-Alvarez E, González-Hernández G, Romero F, Lozano R, Beramendi-Orosco LE. Assessment of heavy metal pollution in urban topsoils from the metropolitan area of Mexico City. J Geochem Explor 2009;101:218–24. Nielsen FH. In: Goyer RA, Mehlman MA, editors. Nickel toxicity. Toxicology of trace elementsWashington: Hemisphere Pub. Corp.; 1977. p. 129–46. Chapter 5. Peryea FJ. Phosphate-induced release of arsenic from soils contaminated with lead arsenate. Soil Sci Soc Am J 1990;55(5):1301–6. Peryea FJ, Kammereck R. Phosphate-enhanced movement of arsenic out of lead arsenate-contaminated topsoil and through uncontaminated subsoil. Water Air Soil Pollut 1997;93(1–4):243–54. Phinney JT, Bruland KW. Trace metal exchange in solution by the fungicides Ziram and Maneb (dithiocarbamates) and subsequent uptake of lipophilic organic zinc, copper

481

and lead complexes into phytoplankton cells. Environ Toxicol Chem 2009;16(10): 2046–53. Querol X, Alastuey A, López-Soler A, Mantilla E, Plana F. Mineralogy of atmospheric particulates around a large coal-fired power station. Atmos Environ 1996;30(21):3557–72. Sezgin N, Ozcan HK, Demir G, Nemlioglu S, Bayat C. Determination of heavy metal concentrations in street dusts in Istanbul E-5 highway. Environ Int 2003;29:979–85. Škrbic B, Ðurišic-Mladenovic N. Chemometric interpretation of heavy metal patterns in soils worldwide. Chemosphere 2010;80:1360–9. Stanek EJ, Calabrese EJ, Barnes RM. Soil ingestion estimates for children in Anaconda using trace element concentrations in different particle size fractions. Hum Ecol Risk Assess 1999;5:547–58. Teng Y, Ni S, Wang J, Zuo R, Yang J. A geochemical survey of trace elements in agricultural and non-agricultural topsoil in Dexing area, China. J Geochem Explor 2010;104: 118–27. U.S. EPA. Reference dose (RfD): description and use in health risk assessments, background document 1A, Integrated Risk Information System (IRIS). Washington, D.C.: United States Environmental Protection Agency; 1993. 15 March, http:// www.epa.gov/iris/rfd.htm. United Nations, Food and Agricultural Organization (FAO). State of the World's Forests; 1997. Rome. Wang X, Qin Y, Chen Y. Heavy metals in urban roadside soils, part 1: effect of particle size fractions on heavy metals partitioning. Environ Geol 2006;50:1061–6. Waters MD. In: Goyer RA, Mehlman MA, editors. Toxicology of vanadium. Toxicology of trace elementsWashington: Hemisphere Pub. Corp; 1977. p. 147–89. chapter 6. Wei B, Yang L. A review of heavy metal contaminations in urban soils, urban road dusts and agricultural soils from China. Review article. Microchem J 2010;94:99-107. Wei B, Jiang F, Li X, Mu S. Spatial distribution and contamination assessment of heavy metals in urban dust from Urumqi, NW, China. Microchem J 2009;93:147–52. Yaqin J, Feng Y, Jianhui W, Tan Z, Zhipeng B, Chiqing D. Using geoaccumulation index to study source profiles of soil dust in China. J Environ Sci 2008;20:571–8. Young WD, Rutzke M, Gutenmann WH, Lisk DJ. Nickel and vanadium in foliage in the vicinity of an oil-fired power plant. Chemosphere 1993;27(7):1269–72.