The relationship between magnetic parameters and heavy metal contents of indoor dust in e-waste recycling impacted area, Southeast China

Science of the Total Environment 433 (2012) 302–308

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The relationship between magnetic parameters and heavy metal contents of indoor dust in e-waste recycling impacted area, Southeast China Zongmin Zhu a, b,⁎, Zhixuan Han c, Xiangyang Bi a, b, Wenlin Yang b a b c

State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China Faculty of Earth Science, China University of Geosciences, Wuhan 430074, China Institute of Geophysical and Geochemical Exploration, Langfang 065000, China

a r t i c l e

i n f o

Article history: Received 14 March 2012 Received in revised form 5 June 2012 Accepted 19 June 2012 Available online 15 July 2012 Keywords: Environmental magnetism Heavy metals Dust E-waste recycling

a b s t r a c t Environmental contamination due to uncontrolled e-waste recycling is an emerging global problem. The aim of this study is to test the applicability of magnetic methods for detecting the metal pollutants emitted from e-waste recycling activities. Dust samples collected from a typical e-waste recycling region in Guiyu, Guangdong Province, China, were investigated using magnetic, geochemical, micro-morphological and mineralogical analysis. The values of mass-specific susceptibility (χ) and saturation isothermal remanent magnetization (SIRM) in dusts from e-waste recycling impacted areas ranged from 101 to 636 × 10 −8 m 3 kg−1 and from 10.5 to 85.2 × 10 −3 Am2 kg−1, respectively. There was a significant correlation between SIRM and χ (r 2 = 0.747, p b 0.001), indicating that ferrimagnetic minerals were dominating χ in the dust samples. The values of χfd% varied from 2.6 to 4.6% with a mean of 3.4%, which suggested that magnetic carriers in the dusts are predominately coarse-grained particles. Two shapes of magnetic particles, spherule (10–150 μm) and angular-shaped particles (30–300 μm), were identified by scanning electron microscope (SEM) and energy dispersive X-ray spectrometer (EDX) analyses. κ-T curves, magnetic hysteresis loops and X-ray diffraction (XRD) analysis indicated that these magnetic particles were magnetite and goethite. There were significant correlations between SIRM and heavy metals (especially Cd, Co, Fe, Ni and Zn) as well as the Tomlinson pollution load index (PLI) of the dust, indicating that SIRM can be used as an efficient proxy for metal pollution in the e-waste recycling impacted area. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Heavy metal pollutants emitted from different industrial activities have a direct influence on the quality of life and health risks. The contaminated extent and toxicity of heavy metals depend on their concentrations and speciation, which were generally assessed by geochemical analysis (e.g. AAS, ICP-AES, ICP-MS). Compared to the relatively complex, time-consuming and expensive geochemical approaches, environmental magnetic methods are simple, rapid, and have low-cost and non-destructive characteristics. These techniques are based on the fact that heavy metal pollution in many cases is accompanied by emissions of ferromagnetic/ferrimagnetic particles because of the abundant presence of Fe in natural resource materials (Jordanova et al., 2003). Once a clear relationship is established in a specific area between magnetic and nonmagnetic data, magnetic measurements become a valuable tool for better and faster determination of sampling sites and monitoring temporal changes in metal ⁎ Corresponding author at: State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China. Tel.: +86 27 67883001; fax: +86 27 67883002. E-mail address: [email protected] (Z. Zhu). 0048-9697/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.scitotenv.2012.06.067

pollution (Davila et al., 2006). Based on quantified relationships between magnetic parameters (e.g., magnetic susceptibility and saturation isothermal remanence (SIRM)) and heavy metal concentrations constructed by appropriate indexes, magnetic measurements have been successfully applied to quantify the levels of heavy metals in many environmental samples, such as soils (Jordanova et al., 2003; Spiteri et al., 2005; Yang et al., 2007a; Kapička et al., 2008; Blundell et al., 2009; Rosowiecka and Nawrocki, 2010), road dust (Kim et al., 2009; Yang et al., 2010; Bućko et al., 2010, 2011; Qiao et al., 2011; Wang et al., 2012), sediments or sludge (Chaparro et al., 2004; Yang et al., 2007b; Zhang et al., 2007, 2011; Rijal et al., 2010; Bijaksana and Huliselan, 2010), and tree leaves or mosses (Matzka and Maher, 1999; Jordanova et al., 2003; Davila et al., 2006; Zhang et al., 2006; Maher et al., 2008; Salo et al., 2012). Furthermore, magnetic properties of particles may contain information on their origins and thus can be used to identify natural and various anthropogenic emission sources. Generally, anthropogenic magnetic particles are dominated by coarse multidomains (MD, grain size > 150–200 nm) and stable single-domain (SSD, grain size between 50 and 150 nm) grains, while pedologically originating particles are usually finer superparamagnetic (SP, grain size b 30–40 nm) grains (Hay et al., 1997; Davila et al., 2006). Typically, for magnetic particles originating

Z. Zhu et al. / Science of the Total Environment 433 (2012) 302–308

from coal combustion, the distinctive feature is their characteristic spherical shape (Blaha et al., 2008; Zhang et al., 2009; Magiera et al., 2011), while magnetic particles derived from vehicle and smelting emissions have been identified as a non-spherical aggregate (Matzka and Maher, 1999; Abdul-Razzaq and Gautam, 2001; Maher et al., 2008; Zhang et al., 2006, 2009; Yang et al., 2010). Environmental contamination due to uncontrolled e-waste recycling is an emerging global problem (Robinson, 2009). In 2006, the world's production of E-waste was estimated at 20–50 million tonnes per year, and it is growing at around 4% per year (Focus, 2005). It is estimated that 50–80% of the e-waste from US, Europe, and other areas of the world is legally or illegally exported to Asia each year, and 90% of which is transported to China (Chen et al., 2009). E-waste contains large quantities of hazardous chemicals (e.g., heavy metals and POPs), which may be released during uncontrolled e-waste recycling processes, threatening the ecosystem and the health of local residents (Li et al., 2007; Wong et al., 2007; Leung et al., 2008; Chen et al., 2009; Robinson, 2009; Ngoc et al., 2009; Luo et al., 2011, Huo et al., 2007, Bi et al., 2011). Guiyu town in Shantou, Guangdong Province, China, is one of the most notorious e-waste recycling regions in the world. Previous studies found that surface dust from this area had been seriously contaminated by heavy metals (Leung et al., 2008; Bi et al., 2011) and resulted in elevated blood lead levels (BLLs) in children living in the local environment (Huo et al., 2007). In order to rapidly assess the environmental impact and temporal change of the e-waste recycling activities, a simple and effective method needs to be developed, and magnetic measurement may be a better choice. However, no such work has been reported so far. Therefore, the major objectives of this study are to determine the magnetic characteristics of dust from the e-waste recycling area in Guiyu and to build up a possible link between the enhanced concentration of e-waste recycling-related magnetic particles and heavy metals. 2. Materials and methods 2.1. Dust sample collection A detail description of the studied area and sample collection is given by Bi et al. (2011). In brief, two kinds of family-run workshops in Guiyu were investigated in this study. One is engaged with printed circuit boards (PCB) from computers and other large appliances, which are dismantled with a hammer and melted over honeycombed coal blocks (named PCB baking), releasing valuable electronic components, such as diodes, resistors, and microchips. The other is plastic processing, including plastic scraps sorting and grinding (Huo et al., 2007). 29 indoor dust samples were collected from 13 villages in Guiyu. Dust samples were collected inside from the floor using a brush and a plastic spatula, stored in polyethylene bags, and then transported to the laboratory. All samples were air-dried at room temperature, and passed through a 2 mm sieve to remove rocks, plants, hair and other impurities. The homogenized dust samples were ground to a fine powder texture with an agate mortar prior to chemical analyses. 2.2. Experimental methods Volume magnetic susceptibility (κ) of the dust was measured with a kappabridge KLY-3 (AGICO, Brno) at 875 Hz operating frequency and 300 Am −1 field intensity, and it was calculated as a mass-specific susceptibility (χ) in 10 −8 m 3kg −1. Magnetic susceptibility at low (χlf, 976 Hz) and high (χhf, 15,616 Hz) frequencies were measured with a kappabridge MFK1-FA (AGICO, Brno) at 200 Am −1 field intensity. Frequency-dependent susceptibility (χfd%) was then calculated and expressed as a percentage χfd% = (χlf − χhf) / χlf × 100%. An isothermal remanent magnetization (IRM) experiment was performed with an ASC Scientific (Model IM-10) impulse magnetizer and Molspin

303

magnetometer. The IRM acquired in a field of 1.0 T was regarded as saturation IRM (SIRM). The samples were then magnetized in a backward field of 300 mT to get IRM−300 mT. S-ratio was calculated as IRM−300 mT / SIRM. The temperature-dependence of the low-field magnetic susceptibility curve of two typical dust samples (i.e., GD1 and GD14 nearby concentrated PCB baking workshops and plastic processing workshops, respectively) was conducted with a Kappabridge KLY-4 equipped with a CS-4 high temperature furnace in an argon atmosphere. The hysteresis loop of these two samples was measured at room temperature with a Micromag vibrating magnetometer (Model 3900) at the Institute of Geology and Geophysics in Beijing. The saturation magnetization at 1 T (Ms), saturation remanence (Mrs), and the coercivity (Bc) were obtained following subtraction of the paramagnetic contribution. Remanence coercivity (Bcr) was obtained by back-field demagnetization curves (Zhang et al., 2011). After obtaining the magnetic measurements of the bulk samples, the magnetic particles were separated from the dust samples (GD1 and GD14) using a hand magnet and were used for morphology and mineralogy analysis. To evaluate the extraction efficiency, the χ and SIRM were measured before and after the magnetic extractions (Maher et al., 2003; Kim et al., 2009), the χ and SIRM values of the magnetic extracts were > 96% of those prior to extraction, indicating that the magnetic particles in the dusts were effectively separated. For morphology analysis, the extracts were fixed by gum and covered by a gold layer, and then analyzed with an environmental scanning electron microscope (SEM) (Model Quanta 200) equipped with an energy dispersive X-ray spectrometer (EDX) microanalyzer (analytical condition of 20 kv of accelerating voltage and 2 × 10−9 A of beam current). Mineralogy of the magnetic extracts was characterized using a powder X-ray diffractometer (XRD) (X'Pert PRO DY2198, PANalytical Inc.). The diffraction pattern was recorded from 3° to 65°. About 0.25 g of the prepared dust sample was digested with a concentrated HNO3–HClO4–HF–HCl mixture. The concentrations of common heavy metals of the digested solution were determined by an inductively coupled plasma-atomic emission spectrometer (ICP-AES). QA/QC included reagent blanks, analytical duplicates, and analysis of the standard reference material (SRM) (SRM 2704 and 1648). The recovery rates for the considered metals in the SRM were between 75 and 115%. 2.3. Statistical analysis The data were statistically analyzed using the statistical package, SPSS v13.0 (SPSS Inc.). The correlation analysis between magnetic parameters and metal concentrations was conducted by a Pearson correlation, and the level of significance was set at p b 0.01 and p b 0.001 (two-tailed). Principal component analysis (PCA) was conducted using factor extraction with Eigenvalues > 1 after varimax rotation. 3. Results and discussion 3.1. Magnetic properties The results of basic magnetic parameters, including χ, χfd%, SRIM and S-ratio, are showed in Table 1. The values of χ in the dust ranged from 101 to 636×10−8 m3kg−1 with a mean of 329×10−8 m3kg−1, which were notably lower than the values of coal fly ash (579×10−8 m3kg−1 for lignite and 3506×10−8 m3kg−1 for hard coal) (Magiera et al., 2011) and street dust close to iron and steelworks (~800–1400× 10−8 m3kg−1) (Zheng and Zhang, 2007), but higher than those of car exhaust particulates (180×10−8 m3kg−1) (Lu et al., 2005) and cement dust (146×10−8 m3kg−1) (Magiera et al., 2011). The range of the SIRM of the dust was 10.5–85.2×10−3 Am2kg−1 and the mean value was 39.4×10−3 Am2kg−1. There was a significant correlation between SIRM and χ (r2 =0.747, pb 0.001) (Fig. 1), indicating that ferrimagnetic minerals were dominating χ in our dust samples.

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Table 1 Summary of magnetic properties of dust. Magnetic properties

Sampling areas

χ (×10−8 m3 kg−1) SIRM (×10

−3

2

Am kg

−1

Mean (range) Median Mean (range) Median Mean (range) Median Mean (range) Median

)

χfd (%) S-ratio

Total (n = 29)

A (n = 11)

B (n = 13)

C (n = 5)

362 (101–636) 389 47 (11–85) 51 3.1 (2.6–3.7) 3.1 0.95 (0.90–1.0) 0.94

312 (144–623) 268 35 (17–68) 32 3.5 (2.8–4.2) 3.5 0.93 (0.85–0.98) 0.92

299 (236–472) 264 33 (30–44) 31 3.7 (2.8–4.6) 3.6 0.91 (0.89–0.95) 0.91

329 (101–636) 272 39.4 (11–85) 31.9 3.37 (2.6–4.6) 3.32 0.94 (0.85–1.0) 0.94

A: PCB baking workshops; B: plastic processing workshops; C: the house has not been used as a workshop for e-waste recycling.

The values of SIRM and χ in the dust from PCB baking workshops were higher than those from plastic processing workshops (Table 1), which together with higher heavy metal concentrations (shown below) indicated that the former activities would release more metal pollutants. The χfd% is sensitive to the superparamagnetic (SP) component, if χfd% > 4%, the assemblage of the magnetic grains contains a significant portion of SP particles, whereas χfd% b 4% indicates a low proportion of SP particles (Dearing et al., 1996). The values of χfd% in our samples varied from 2.6 to 4.6% with a mean of 3.4%, which suggested that magnetic carriers in the dust are predominately coarse-grained particles and the proportion of SP particles is much lower. However, this data are higher than those of many anthropogenic sources, such as power plant fly ash (1.3%) (Kapička et al., 2000), and car exhaust particulates (0.01%) (Lu et al., 2005), indicating that dust from e-waste recycling may contain finer magnetic particles than those from above mentioned sources. IRM acquisition and back-field demagnetization curves for representative samples are shown in Fig. 2. The measured samples rapidly acquired IRM at low fields (b 100 mT) and a near saturation at ~ 300 mT, while back-field demagnetization curves display a softer behavior with Bcr values of 30–35 mT. This pattern indicated a predominance of low-coercivity Fe-oxides (e.g., Fe3O4 and γFe2O3) (Kim et al., 2009). High S-ratio, from 0.85 to 1.0 (mean: 0.94), also confirmed the prominence of soft, low-coercivity magnetite-type ferrimagnetic minerals (Table 1). The hysteresis loops are thin, closed and approach magnetic saturation in the field of about 300 mT (Fig. 3), further indicating that the magnetic minerals in the dust were dominated by low coercivity ferrimagnetic minerals. Magnetic minerals in the dust can be identified by temperaturedependent susceptibility (κ-T) cycles (Fig. 4). Two ferrimagnetic phases in the dust samples can be detected from the changes in slope and the clear drop of the signal during heating. One with a slightly increasing susceptibility at 280–300 °C might reveal the

presences of maghemite and/or iron hydroxides. The later would have undergone dehydration at these temperatures. The second having a curie temperature of 580 °C strongly indicated the existence of magnetite. The magnetite phase is best expressed and comprises a major part of the magnetic susceptibility in the dust as shown in Fig. 4. The cooling curves showed a significant increase in susceptibility for temperatures b580 °C compared with the heating curves, which demonstrated the formation of a new magnetite during heating (Yang et al., 2007b; Zhang et al., 2010).

3.2. SEM-EDX and XRD analysis The use of environmental SEM to observe iron particles magnetically separated from dust impacted by e-waste recycling revealed that there were two major groups of iron oxides among the magnetic phase (Fig. 5). One was magnetic spherule with different grain sizes from about 10 to hundreds of micrometers (with a typical diameter of 10– 150 μm). Most of them have a smooth surface. In some cases spherules were empty inside with noticeable holes remaining after gas outlet (Fig. 5f). EDX analysis revealed the concentrations of Fe in these magnetic spherules ranging from 59 to 83%. These iron spherules are typical products of combustion (coal fly ash) during the e-waste recycling process. The second was identified as irregular-shaped magnetic particles with grain sizes reaching as much as several hundreds of micrometers (with a typical diameter of 30–300 μm). The concentrations of Fe in these particles varied greatly, from 6.2 to 86%. This material is probably associated with the erosion of the e-wastes (circuit boards). Obvious peaks of silicon and calcium were noticed in some cases of the particles (Fig. 5e, f), probably indicating the existence of an amorphous silica coater and calcium ferrites (Magiera et al., 2011).

IRM/SIRM

SIRM (× 10-3 Am2 kg-1)

1.2 90

1.0

80

0.8

70

0.6

60 50

0.4

40

0.2

30 -40

20

0.0 -20 -0.2

10 0

GD1 GD14

200

400

600

800

1000 1200 1400

Applied field (mT)

-0.4 0

100

200

300

400

500

χ (× 10-8 m3 kg-1) Fig. 1. Correlation between χ and SIRM.

600

700

-0.6 Fig. 2. Isothermal remanent magnetization (IRM) acquisition and back-field demagnetization curves for selected dust samples.

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Fig. 3. Magnetic hysteresis loops for selected dust samples.

X-ray diffraction analysis of magnetic extracts from dust confirmed the results of magnetic parameters. The X-ray diffraction peaks matched with the diffraction lines of magnetite (Fe3O4), goethite (αFeO(OH)), quartz (SiO2) and calcite (CaCO3), respectively (Fig. 6). This demonstrated the fact that the ferrimagnetic minerals were dominated by magnetite and goethite. 3.3. Heavy metal concentrations Eleven heavy metals, including Ag, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V and Zn, were analyzed in this study and the summary of their concentrations was listed in Table 2. The concentrations of most of the heavy metals were extremely elevated and varied greatly in the dust samples. For example, Ag varied 378 folds from 1.0 to 378 mg kg−1, Cu 1063 folds from 56 to 59,500 mg kg−1, and Pb 165 folds from 86 to 14,200 mg kg−1. However, the enrichments and variations of Co (4.0–13 mg kg−1), Mn (333–1240 mg kg−1) and V (19–63 mg kg−1) were not as high as the others. Leung et al. (2008) also reported extremely high concentrations of Pb (110,000 mg kg−1), Cu (8360 mg kg−1), Zn (4420 mg kg−1)

7 6

GD1

κ/κ0

5 4 3 2 1 0 0

100

200

300

400

500

600

700

800

T(oC)

3.4. Relationships of heavy metal concentrations and magnetic properties

4

GD14 κ/κ0

3 2 1 0

and Ni (1500 mg kg−1) in surface dust from the same region. The highest concentrations of heavy metals were found in samples from PCB baking workshops (Table 2). On the contrary, dusts from houses where e-waste workshops were absent had the lowest concentrations for most of the metals except for Cu and Zn, which were still highly elevated (Table 2). The above result indicates that the studied area has been seriously contaminated by heavy metals as a result of e-waste recycling activities. The high concentrations of toxic metals in dust will pose a severe health threat to local inhabitants, especially children, due to the involuntary or direct ingestion of contaminated dust particles via the “hand to mouth” pathway. In order to assess the relative heavy metal toxicity and how much in a sample exceeds the normal concentrations, the Tomlinson pollution load index (PLI) was calculated based on each of the metal concentrations (Angulo, 1996). The PLI index is defined as the nth root of the multiplication of the concentration factors (CF): PLI = n√(CF1 × CF2 × CF3 × …CFn), where CF is the ratio between the concentration of each heavy metal and its corresponding background value or the lowest concentration value detected for each heavy metal. According to Singh et al. (2003) PLI values vary from 0 (unpolluted) to 10 (highly polluted) as follows: 0 b PLI ≤ 1 unpolluted; 1 b PLI ≤ 2 moderately polluted to unpolluted; 2 b PLI ≤ 3 moderately polluted; 3 b PLI ≤ 4 moderately polluted to highly polluted; 4 b PLI ≤ 5 highly polluted; PLI > 5 very highly polluted. In the present study, the local background (Guangdong Province) concentrations of metals in soils (CEMS, 1990) were used to calculate the PLI. As shown in Table 2, the PLI values of the dust ranged from 1.7 to 28. Most samples from PCB baking workshops were very highly polluted. Dusts from plastic processing workshops were between moderately to highly polluted and highly polluted. While samples from houses where e-waste workshops were absent were moderately to highly polluted.

0

100

200

300

400

500

600

700

800

T(oC) Fig. 4. Temperature-dependence of magnetic susceptibility (κ-T) heating (black line) and cooling (gray line) curves for selected dust samples. Each curve was normalized with its corresponding magnetic susceptibility at room temperature (κ0).

The data set displays a wide range of values for several heavy metals (Ag, Cr, Cu, Ni, Pb and Zn) and PLI (Table 2). Log10 is employed to prevent prohibitive levels of skew in the data set and permit parametric analyses. The results of the Pearson correlation analysis of heavy metal concentrations and magnetic properties are showed in Table 3. Most of the considered metals, except Mn and V, were correlated significantly with each other, indicating their common origins derived from the e-waste recycling activities. Among the magnetic properties, SIRM was most significantly correlated with Cd, Co, Fe, Ni and Zn, as well as the PLI (r = 0.808–0.946) and less significantly correlated with Ag, Cr, Cu and Pb (r = 0.467–0.658). However, no obvious correlations were found between SIRM and Mn and V. These relationships suggested that magnetic particles and heavy metals did coexist in dust emitted from e-waste recycling, and SIRM

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a

1 C: 11% O: 35% Fe: 51%

1

1 C: 7% O: 19% Al: 2% Si: 5% Fe: 65%

b

2

1 2 C: 4% O: 7% Fe: 86%

2

2 C: 6% O: 26% Fe: 65%

d

c

3

3 C: 7% O: 18% Fe: 69% 4 C: 6% O: 11% Si: 14% Fe: 53%

C:12% O: 24% Fe: 59%

2 1

1 C: 6% O: 14% Fe: 76% 2 C: 7% O: 14% Fe: 75%

4

e

3

1 C: 7%, O: 15% Ca: 3% Fe: 70% 2 C: 6%, O: 15% Fe: 75%

1

4

f 2 3 C: 14%, O: 12% Si: 3% Ca: 8% Fe: 60% 4 C: 28%, O: 26% Si: 21% Ca: 9% Fe: 6%

1 O: 11%, Fe: 83% 2 O: 26%, Fe: 67% 3 O: 17%, Si: 3% Ca: 7%, Fe: 68%

1

2 3 4

4 C: 13%, O: 18% Si: 5%, Ca: 13% Fe: 42%

Fig. 5. SEM photographs and chemical compositions (based on EDX) of magnetic extracts from selected dust samples. Images (a), (b) and (c) are from dust GD1; images (d), (e) and (f) are from dust GD14. Crosses indicate the spots of EDX analysis.

can be used as an efficient proxy for heavy metal pollution (especially Cd, Co, Fe, Ni and Zn) in e-waste recycling impacted area. Furthermore, the correlation between SIRM and PLI indicated that magnetic

concentration is more proportional to the concentrations of selected metals rather than each individual concentration, and that the comprehensive evaluation of heavy metals is more reasonable (Yang

Fig. 6. X-ray diffraction pattern of magnetic extracts obtained from samples of GD 1 and GD 14. M—magnetite, G—goethite, Q—quartz, C—calcite, F—feldspar.

Z. Zhu et al. / Science of the Total Environment 433 (2012) 302–308

307

Table 2 Summary of heavy metal concentrations of dust. Heavy metals

Sampling areas

Ag (mg kg−1) Cd (mg kg Co (mg kg

−1

Mean (range) Median Mean (range) Median Mean (range) Median Mean (range) Median Mean (range) Median Mean (range) Median Mean (range) Median Mean (range) Median Mean (range) Median Mean (range) Median Mean (range) Median Mean (range) Median

)

−1

)

Cr (mg kg−1) Cu (mg kg−1) Fe (g kg−1) Mn (mg kg−1) Ni (mg kg−1) Pb (mg kg−1) V (mg kg−1) Zn (mg kg−1) PLI

Total (n = 29)

A (n = 11)

B (n = 13)

C (n = 5)

45 (1.0–378) 4.1 2.4 (0.5–4.3) 2.9 7.9 (4.0–13) 8.0 67 (24–188) 60 6420 (123–59,500) 469 33 (14–65) 37 761 (333–1020) 850 108 (11–401) 97 1910 (141–12,400) 581 37 (19–54) 33 1460 (263–3410) 850 8.0 (1.7–28) 6.4

4.4 (1.0–12) 2.9 1.7 (0.8–2.8) 1.8 6.2 (4.0–8.0) 6.0 56 (40–66) 54 388 (105–1050) 172 25 (15–38) 23 800 (544–1120) 743 52 (22–155) 33 323 (163–721) 253 43 (30–56) 42 1060 (528–2270) 840 4.5 (3.2–6.0) 4.4

1.1 (0.9–1.2) 1.1 1.2 (0.8–2.0) 1.0 5.4 (4.0–7.0) 5.0 60 (51–71) 61 786 (56–3590) 96 24 (21–32) 23 1090 (837–1240) 1150 28 (18–48) 23 131 (86–173) 122 57 (48–63) 59 547 (469–661) 533 3.3 (2.9–3.9) 3.1

19.1 (1.0–378) 2.2 1.9 (0.5–4.3) 1.8 6.7 (4.0–13) 6.0 61 (20–188) 60 2740 (56–59,500) 225 28 (14–65) 23 836 (333–1240) 855 69 (11–401) 33 892 (86–12,400) 253 43 (19–63) 45 1120 (263–3410) 770 5.6 (1.7–28) 4.1

A: PCB baking workshops; B: plastic processing workshops; C: the house has not been used as a workshop for e-waste recycling.

et al., 2010). In comparison with SIRM, the correlations between χ and heavy metals were less significant. The significant correlations were only found between χ and Cd, Co, Fe, Ni and Zn. This finding was in agreement with many of the previous studies (Yang et al., 2010; Zhang et al., 2011; Wang et al., 2012) and suggested that SIRM is a more efficient indicator than χ for heavy metals in dust due to the fact that anthropogenic emitted particles are generally ferrimagnetic minerals. The relationships of heavy metal concentrations and magnetic properties can be further revealed by the PCA result (Table 4). The metal composition, together with the magnetic properties, was dominated by three principal components (PCs) that explain 91.0% of the total variances. PC1 explained 42.4% of the total variances with higher loadings of Cd, Co, Fe, Ni, Zn, χ and SIRM. PC2 was related to Ag, Cu, Ni and Pb, accounting for 32.3% of the variances. Manganese and V were associated with PC3, accounting for 16.3% of the variances. This finding clearly revealed that the e-waste recycling activities can emit two groups of heavy metal pollutants. One is characterized by Cd, Co, Fe, Ni and Zn, which showed positive and significant linear correlations with magnetic parameters (SIRM and χ). The other

group is Ag, Cu, Ni and Pb, which showed a slight correlation with SIRM. Finally, Mn and V do not correlate with any of the magnetic parameters, or with the other heavy metals, suggesting that the presence of these metals in our samples was not derived from the e-waste recycling emissions. 4. Conclusions The results of this study indicated the coexistences of magnetic particles and heavy metals in dust from the e-waste recycling impacted area. The magnetic methods, together with micro-morphological and mineralogical analyses (SEM-EDX and XRD), revealed that there were two shapes of magnetic particles (magnetite and goethite), spherule (10–150 μm) and angular-shaped particles (30–300 μm), dominating the magnetic minerals in the dust. Pearson correlation analysis and principal component analysis (PCA) showed that SIRM was significantly correlated with the heavy metals as well as the PLI, indicating that SIRM can be used as an efficient proxy for heavy metal pollution in the e-waste recycling impacted area. In comparison with SIRM, the relationships between χ and heavy metals were less

Table 3 Pearson correlation coefficients (r) of heavy metals and magnetic properties. Log10Ag Cd Co Log10Cr Log10Cu Fe Mn Log10Ni Log10Pb V Log10Zn Log10PLI χ SIRM Xfd ⁎

0.724⁎⁎ 0.785⁎⁎ 0.714⁎⁎ 0.682⁎⁎ 0.735⁎⁎

Cd

Co

0.152 0.671⁎⁎ 0.877⁎⁎

0.924⁎⁎ 0.662⁎⁎ 0.530⁎⁎ 0.938⁎⁎ 0.036 0.944⁎⁎ 0.817⁎⁎

0.668⁎⁎ 0.610⁎⁎ 0.933⁎⁎ 0.040 0.896⁎⁎ 0.860⁎⁎

−0.268 0.567⁎ 0.903⁎⁎ 0.295 0.598⁎ −0.015

−0.399 0.842⁎⁎ 0.898⁎⁎ 0.744⁎⁎ 0.922⁎⁎ −0.324

−0.417 0.763⁎⁎ 0.915⁎⁎ 0.635⁎⁎ 0.844⁎⁎ −0.271

Significant level at p b 0.01 (two-tailed). ⁎⁎ Significant level at p b 0.001 (two-tailed).

Log10Cr

Log10Cu

0.568⁎ 0.733⁎⁎ 0.578⁎ 0.596⁎ 0.644⁎⁎

0.610⁎⁎ −0.005 0.527⁎⁎ 0.745⁎⁎

0.097 0.475⁎ 0.792⁎⁎ 0.400 0.644⁎⁎ 0.221

−0.436 0.450 0.777⁎⁎ 0.215 0.467⁎ −0.127

Fe

Mn

0.096 0.929⁎⁎ 0.800⁎⁎ −0.375 0.743⁎⁎ 0.903⁎⁎ 0.759⁎⁎ 0.946⁎⁎ −0.292

−0.051 −0.092 0.795⁎⁎ −0.054 0.147 0.024 0.105 0.489⁎

Log10Ni

0.772⁎⁎ −0.482⁎ 0.881⁎⁎ 0.871⁎⁎ 0.819⁎⁎ 0.941⁎⁎ −0.391

Log10Pb

−0.568⁎ 0.700⁎⁎ 0.920⁎⁎ 0.371 0.658⁎⁎ −0.092

V

Log10Zn

Log10PLI

χ

SIRM

−0.482⁎ −0.363 −0.227 −0.322 0.421⁎

0.784⁎⁎ 0.684⁎⁎ 0.808⁎⁎ −0.348

0.558⁎ 0.815⁎⁎ −0.161

0.865⁎⁎ −0.460

−0.367

308

Z. Zhu et al. / Science of the Total Environment 433 (2012) 302–308

Table 4 Principal component analysis results for heavy metals and magnetic properties. Component

Log10Ag Cd Co Log10Cr Log10Cu Fe Mn Log10Ni Log10Pb V Log10Zn χ SIRM Eigenvalues % of variancce Cumulative %

PC1

PC2

PC3

0.333 0.825 0.706 0.425 0.160 0.797 0.033 0.875 0.429 −0.258 0.786 0.956 0.915 5.52 42.4 42.4

0.864 0.513 0.634 0.669 0.854 0.558 0.105 0.439 0.850 −0.331 0.370 −0.023 0.346 4.19 32.3 74.7

0.078 −0.037 −0.047 0.527 −0.123 0.027 0.972 −0.130 −0.202 0.883 −0.176 0.009 0.042 2.12 16.3 91.0

Extract method: principal component analysis. Rotation method: varimax with Kaiser normalization. Variances with loadings higher than 0.6 were in bold.

significant. These findings of our study concurrently demonstrate that the measurement of magnetic properties is a simple, rapid, efficient and nondestructive method for the assessment of heavy metal contamination.

Acknowledgments This study was financial supported by the Natural Science Foundation of China (40904015, 40903041 and 40525008).

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