Environmental Pollution 159 (2011) 3057e3070
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Assessment of heavy metal pollution from a Fe-smelting plant in urban river sediments using environmental magnetic and geochemical methods Chunxia Zhang a, *, Qingqing Qiao a, John D.A. Piper b, Baochun Huang a a b
State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, No. 19 Bei Tucheng Xilu, Chaoyang Dist., Beijing 100029, China Geomagnetism Laboratory, Department of Earth and Ocean Science, University of Liverpool, Liverpool L69 7ZE, UK
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 November 2010 Received in revised form 1 April 2011 Accepted 7 April 2011
Environmental magnetic proxies provide a rapid means of assessing the degree of industrial heavy metal pollution in soils and sediments. To test the efficiency of magnetic methods for detecting contaminates from a Fe-smelting plant in Loudi City, Hunan Province (China) we investigated river sediments from Lianshui River. Both magnetic and non-magnetic (microscopic, chemical and statistical) methods were used to characterize these sediments. Anthropogenic heavy metals coexist with coarse-grained magnetic spherules. It can be demonstrated that the Pollution Load Index of industrial heavy metals (Fe, V, Cr, Mo, Zn, Pb, Cd, Cu) and the logarithm of saturation isothermal remanent magnetization, a proxy for magnetic concentration, are significantly correlated. The distribution heavy metal pollution in the Lianshui River is controlled by surface water transport and deposition. Our findings demonstrate that magnetic methods have a useful and practical application for detecting and mapping pollution in and around modern industrial cities. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Environmental magnetism Heavy metals River sediments Industrial plant Hunan China
1. Introduction Heavy metal pollution of aquatic ecosystems is becoming a growing global problem as population increase, and urbanization and industrialization expand. A consequence of the growth of heavy industry has been the addition of high concentrations of heavy metals originating from anthropogenic inputs including industrial wastewater discharges, sewage wastewater, fossil fuel combustion and atmospheric deposition (Birch et al., 1996; Linnik and Zubenko, 2000; Lwanga et al., 2003; Chaparro et al., 2004; Jordanova et al., 2004; Spiteri et al., 2005; Rijal et al., 2010; Sekabira et al., 2010). Trace amounts of heavy metals are always present in water, and some elements may be immobilized within stream sediments and thus could be involved in absorption, co-precipitation, complex formation, and co-adsorbed with Fe oxides and hydroxides, or other particulate forms (Awofolu et al., 2005; Okafor and Opuene, 2007; Mohiuddin et al., 2010). The development of sensitive tools and procedures for detecting and monitoring water quality and thus avoiding heavy metal poisoning is a pressing task. Heavy metal concentrations in soil and stream sediments can be used to reveal the intensity and range of local and regional pollution. However, traditional geochemical methods (e.g. AAS, ICP-MS)
* Corresponding author. E-mail address:
[email protected] (C. Zhang). 0269-7491/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2011.04.006
are relatively complex, time-consuming and expensive, and are therefore not suitable for performing mapping or monitoring of large-scale pollution. In recent decades, environmental magnetic methods have been widely applied as proxy indicators of environmental pollution because they are simple, rapid, and have lowcost and non-destructive characteristics (Hoffmann et al., 1999; Petrovský et al., 2000; Gautam et al., 2004; Zhang et al., 2008). Measurements of magnetic susceptibility of soil, street dust, sediments etc. have been used to map areas polluted by industrial emissions, such as coal-burning power plants, lead ore smelters, and roadside pollution due to automotive traffic and other atmospheric pollution (Thompson and Oldfield, 1986; Morris et al., 1995; Hay et al., 1997; Strzyszcz and Magiera, 1998; Kapi cka et al., 1999, 2001; 2008; Petrovský et al., 2001; Zhang et al., 2006; Blaha et al., 2008a, 2008b). The relationship between magnetic parameters and heavy metals has been investigated on the proxy assumption that sources of magnetic particles and heavy metals are genetically related. High correlation coefficients between certain pollutants and magnetic susceptibility or SIRM (saturation isothermal remanence) have sometimes been reported (Heller et al., 1998; Petrovský et al., 2001; Chaparro et al., 2004; Jordanova et al., 2004) whilst different correlations between certain magnetic parameters and heavy metals like Pb, Zn, Cu have been detected at single emission sites and can be used for identifying potential sources and discriminating between them (Banerjee, 2003; Jordanova et al., 2003; Al-Khashman, 2004; Lu and
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Bai, 2006; Wang and Qin, 2006; Maher et al., 2008; Lu et al., 2009a,b; Bijaksana and Huliselan, 2010; Bu cko et al., 2010; Yang et al., 2010). In recent years the use of magnetic parameters as proxies for quantifying the contents of certain contaminants such as heavy metals in street dust, atmospheric particles or soil have been demonstrated (Shu et al., 2001; Kim et al., 2007, 2009; Duan et al., 2010). Clear correlations were found between PLI and magnetic concentration parameters for topsoil, stream and marine sediments (Chan et al., 2001; Wang and Qin, 2006; Lu et al., 2007, 2009a,b; Yang et al., 2007; Chaparro et al., 2008; Canbay et al., 2010). These studies prove that quantified relationships between magnetic parameters and heavy metals can be constructed based on appropriate indexes. However, few results have been reported for the detection of heavy metal pollution by surface water transport and deposition in river sediments. This has prompted the present evaluation of whether magnetic parameters can be used as a proxy for such scenarios. In this paper, we compare the results obtained from magnetic parameters and geochemical analyses of heavy metals by integrating results from a spectrum of methods including environmental magnetic, geochemical, electron microscopy and EDX analyses, and incorporating multivariate statistical analysis including of principal component analysis and cluster analysis. The main objectives have been to discriminate between the contributions of different pollution sources and quantifying relationships between magnetic parameters and heavy metal contamination along the Lianshui River near Loudi City in the Hunan Province of China. The study is thus a contribution to the potential use of magnetic measurements and their application for evaluating the scale of industrial pollution caused by surface water transport and deposition in an urban environment. 2. Sampling and laboratory measurements Loudi is a fast-developing industrial city located near the middle of Hunan Province and covers about 426 km2. The major industry operating for fifty years has been a Fe-smelting plant; this main pollution source is located at the north-western side of the city. The landscape of the region comprises low foothills and the Quaternary cover is represented by red loam, with a parent rock of limestone. The east side of the city is a wide plain with altitudes lower than in the West. Lianshui River enters the city from the north-western side, passes by the outside wall of the industrial plant, and then traverses the city center to leave the city at the north-eastern side (Fig. 1a). At the outside of the Fe-smelting plant (sampling sites LDR6-LDR10) there are three wastewater outlets (Fig. 1); OL1 is the wastewater outlet of the Fe-smelting plant. OL2 is the outlet of the power supply unit of the Fe-smelting plant and includes the sintering net ring water system overflow water as well as effluent from small blast furnace water and boiler workshops; the remaining outlet OL3 comes from the cleaning ground and road wastewaters. The rate of wastewater flow at OL2 is about 400e500 m3/h. River sediments were collected along the Lianshui River during July 21e23, 2009, using a gravity sampler. Samples were taken from the upstream section (before entering the city) to the downstream region after leaving the city area; the total sampling distance along the river is about 20 km (Fig. 1a,b). At each sample site, two or three cores were collected and the material was sub-sampled at an interval of 2 cm. In total, 19 sample sites were studied comprising 260 river sediment samples (Fig. 1a). In the laboratory all samples were freeze-dried with lyophilizer and mechanically homogenized and sieved through a 1 mm mesh to remove small stones. Plastic boxes 2 2 2 cm in size were filled with individual samples for magnetic measurements. All sample susceptibility values were measured using a Bartington
MS2 susceptibility meter with 470 Hz operating frequency, and mass-specific susceptibility (c) values were calculated. Anhysteretic remanent magnetizations (ARMs) were imparted in a peak alternating field (AF) of 80 mT with a superimposed direct current (DC) bias field of 0.05 mT parallel to the AF. Isothermal remanent magnetization (IRM) experiments were performed using an impulse magnetizer and the IRM acquired in a field of 1.0 T was regarded as saturation IRM (SIRM). Remanences were measured with a 2G-760 U-channel system. Temperature-dependence of the low-field magnetic susceptibility curves of some samples were conducted using a KLY-3 Kappabridge equipped with a CS-3 hightemperature furnace (sensitivity of 1 108 SI, AGICO Ltd., Brno, Czech Republic) in air atmosphere. All measurements were performed from room temperature up to 700 C with a measurement interval of 2 C and a heating and cooling rate of about 9 C per minute. In addition, hysteresis loops of sediments from four sampling sites (LDR3, LDR11, LDR14 and LDR18) were measured at room temperature using a Model 3900 Micromag vibrating magnetometer. The saturation magnetization at 1 T (Ms), saturation remanence (Mrs, which is equal to SIRM), and the coercivity (Bc) were obtained following subtraction of the paramagnetic contribution. Remanence coercivity (Bcr) was obtained by back-field demagnetization curves. Low-temperature properties of representative samples were measured using a Quantum Design Model XP-5 Magnetic Properties Measurement System (MPMS XP-5, sensitivity 5.0 1010 Am2). Samples were cooled down from 300 to 5 K in a zero magnetic field (ZFC), and saturation remanance acquired in a 5-T field at 5 K (designated SIRM5 T@5 K hereafter) was measured by warming from 5 to 300 K. To determine the SP contents in these samples, temperature dependence of susceptibility was measured from 300 K to 5 K at frequencies of 1 Hz and 1000 Hz. Frequency dependent cfd was calculated from c1 Hz c1000 Hz. All magnetic measurements were carried out in the Paleomagnetism and Geochronology Laboratory in Beijing. Magnetic extracts were obtained from the middle stream river sediment (LDR1, depth of 8e12 cm) using a strong hand magnet sealed with a plastic bag. Afterward, the magnetic extracts were fixed by gum, and their surfaces were coated with gold for scanning electron microscope (SEM). A LEO1450VP was used for SEM observations of the morphology of the particles, and an INCA ENERGY 300 energy dispersive X-ray spectrometer (EDX) was applied to determine elements and composition of the magnetic extracts. The SEM and EDX studies were carried out in the laboratories of the Institute of Geology and Geophysics Beijing. Heavy metal (Be, V, Cr, Co, Ni, Cu, Zn, Rb, Mo, Cd, Cs, Ba, Nd, Pb and Fe) analysis of river sediments from four sampling sites (LDR3, LDR11, LDR14, LDR18) was performed by inductively-coupled plasma-mass spectrometry (ICP-MS) using the DZ/T0223-2001 method with HR-ICP-MS (Element I) Finningan MAT equipment in the Analytical Laboratory of the Beijing Research Institute of Uranium Geology. Pearson’s correlation coefficient analysis, Principal Component Analysis (PCA) and Cluster Analysis (CA) were performed using the commercial statistics software package SPSS version 13.0 (SPSS Inc.) 3. Results 3.1. Magnetic properties The mean c values of surface river silts (0e2 cm) from the 19 sampling sites show low values in the upstream region (typically less than c. 70 108 m3 kg1); strongly enhanced intensities (c. 200e660 108 m3 kg1) in the middle part near the surroundings of the Fe-smelting plant and lower values again
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Fig. 1. (a) Sketch map of the study area (Loudi) and sampling sites; (b) Histogram of mass-specific low-field magnetic susceptibility (c) mean values of surface samples; (c) Isoline plot of c values with a function of distance and depth. Hexagons (OL1, OL2, OL3) are the positions of wastewater outlets.
further downstream (c. 150 108 m3 kg1) (Fig. 1b). Lateral and vertical changes of c values along Lianshui River are more clearly displayed in Fig. 1c which plots the data as a function of distance and depth. Maximum c values are located below 10 cm depth in the middle section. Vertical profiles of c, SIRM, Ms and cARM/c at four sample sites (LDR3, LDR11, LDR14, LDR18) are selected to identify the differences among upstream, middle and downstream sections (Fig. 2). The sediment profile from LDR11 (middle stream section, polluted river sediments) exhibits the largest enhancement of c as its values are four to sixteen times higher than those ( 0.95) are found between each other, indicating that ferrimagnetic minerals are controlling c in these samples (Oldfield, 1991). cARM/c values in profile of LDR3 are a little higher than in other profiles suggesting a higher portion of smaller single domain (SD) to small pseudo-single domain (PSD) particles in LDR3 (Oldfield, 1991). Narrow hysteresis loops that close below w250 mT (Fig. 3a) indicate that low coercivity ferrimagnetic minerals are dominant in these river sediments. A Day-plot of hysteresis parameters of all samples from sites LDR3, LDR11, LDR14 and LDR18 shows that all samples locate within the PSD range (Day et al., 1977; Dunlop, 2002) (Fig. 3b). In the Day diagram, it seems that LDR3 (upstream) values plot more to the left than others indicating a more consistent grain size in the less polluted LDR3. The distribution of middle stream samples (LDR11 and LDR14) is closer to the
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Fig. 2. The vertical profiles of (a) c, (b) SIRM, (c) Ms and (d) cARM/c for all samples from LDR3, LDR11, LDR14 and LDR18, different symbols refer to different sampling sites.
multidomain (MD) range than in the upstream (LDR3) and downstream (LDR18) samples, suggesting that magnetic particles in the middle-stream sediments have relatively coarse-grained PSD sizes. IRM acquisition and back-field demagnetization curves for the above representative samples are shown in Fig. 3c. IRM acquisition curves rapidly reach saturation below 250 mT and are independent of sampling site whilst back-field demagnetization curves display soft behavior with Bcr values of 30e40 mT. All room temperature magnetic measurements indicate that magnetic mineralogy is dominated by low coercivity ferrimagnetic minerals. Magnetic minerals in river sediments can be identified by temperature-dependent susceptibility (c-T) cycles in air (Fig. 4). The increased susceptibility below 250 C and the peaks around 250e300 C may indicate ultrafine particles reaching the SD to superparamagnetic (SP) transition at these temperatures (Oches and Banerjee, 1996; Deng et al., 2004; Liu et al., 2005); the
susceptibility increases sharply to just below the Curie temperature of magnetite (585 C), and shows a Tc of w580 C revealing magnetite as the major contributor to c, in cooling curves c is higher than in heating curves for temperatures < 580 C, which demonstrates new formation of magnetite during heating (Zhang et al., 2010). Magnetic enhancement upon heating of LDR3 is obviously higher than others (Fig. 4). Further proof of the presence of magnetite in surface and deep samples is the clear Verwey transition at w120 K (Fig. 5a,d,g,j). The behavior of frequency-dependence of c0 between 5 and 300 K (Fig. 5b,c,e,f,h,i,k,l) shows that in-phase c0 is different for both frequencies above 120 K. This indicates that SP magnetite particles are present in these river sediments (Kosterov, 2003). An obvious cfd peak between 5 and 80 K (Fig. 5e,f,h,j,l) could be caused by relaxation of domain walls associated with MD magnetite particles (Moskowitz et al., 1993, 1998; Kosterov, 2003; Ba1anda et al., 2005)
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Fig. 3. (a) Magnetic hysteresis loops for representative river sediments following before (dash line) and after (solid line) subtraction of the paramagnetic contribution; (b) Day-plot of the ratios Mrs/Ms and Bcr/Bc for all samples in LDR3, LDR11, LDR14 and LDR18, grain size boundaries for SDePSDeMD are according to Dunlop (2002); (c) IRM acquisition and back-field demagnetization curves of respective samples.
and supporting the dominance of MD magnetite particles in the middle (LDR11, LDR14) and downstream (LDR18) sections. The sharp decrease in remanence and c during warming from 5 to 50 K (Fig. 5aec,j,k) may relate to the main presence of SP particles and paramagnetic minerals in the upstream (LDR3) and at the surface of the downstream (LDR18) sediments (Coey, 1988; Dunlop and Özdemir, 1997). Magnetic results (Figs. 3e5) show that PSDeMD magnetite is the dominant ferrimagnetic phase in the middle stream and
downstream sediments, especially at depths >20 cm, whereas, PSD with SP magnetite is predominant in the upstream sediments; this differences of magnetic mineral phases between different sections of Lianshui River shows that enhancements of magnetic concentration values are controlled by coarse magnetic particles in the sediments. One can conclude that anthropogenic magnetite adds a different grain size fraction to the natural magnetite content which leads to a shift of data points toward “right-down” from the range occurring for uniform grain sizes in Day-plot (Fig. 3b).
Fig. 4. Temperature-dependence of magnetic susceptibility (ceT) heating (solid line) and cooling (dashed line) curves of representative samples in LDR3, LDR11, LDR14 and LDR18 sampling sites. Each curve was normalized with its corresponding magnetic susceptibility at room temperature c0.
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Fig. 5. Low-temperature magnetic measurements for representative samples in LDR3, LDR11, LDR14 and LDR18, respectively; (a, d, g and j) accompanying thermal warming of SIRM5 T@5 K from 5 to 300 K and after cooling in zero field (ZFC) for surface (heavy solid line) and deep (dashed line) sediments, respectively; (b, c, e, f, h, i, k and l) Lowtemperature-dependence of magnetic susceptibility at 1 Hz (light hollow line) and 1000 Hz (heavy solid line) curves (c0 eT) and low-temperature cfdeT curves (dashed line), respectively.
3.2. SEM and EDX Samples from 8 to 12 cm depths at LDR11 (representing the range c of highest anthropogenic input according to maximum c) have been observed and analyzed by SEM and EDX. Results are shown in Fig. 6; magnetic spherules with diameters of 9e140 mm
(Fig. 6aec) and irregular-shaped particles (Fig. 6d) are observed and identified as iron-oxides by EDX analysis. The EDX of spherules surfaces show dominant peaks of Fe, along with smaller peaks of Si or Al (Fig. 6aec, spectrum 1), whereas the EDX of broken sides on spherules show that Si and Al are predominant with small amounts of Fe, Mg, K and Ca etc (Fig. 6bec, spectrum 2). The irregular-shaped
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Fig. 6. SEM images and EDX analysis results of extractor from 8 to 12 cm depths at LDR11.
particles (Fig. 6a, d, spectrum 2) have variable Fe contents (13e70%) with different contents of Si, Al, Mg, Ca, Mn, K etc, and small amounts of Zn are also present in some particles (Fig. 6d). SEM and EDX results from the magnetic spherules are consistent with previous studies of fly ashes and smelting slag (Hoffmann et al., 1999; Xie et al., 2001; Goddu et al., 2004; Jordanova et al., 2004; Gautam et al., 2005; Zhang et al., 2006; Blaha et al., 2008b; Kim et al., 2009; Chaparro et al., 2010; Huliselan et al., 2010; Rosowiecka and Nawrocki, 2010).
3.3. Heavy metals concentrations Concentrations of 15 heavy metals and their mean, standard deviation, minimum and maximum values in different sections of the Lianshui River are given in Table 1. The middle stream section LDR11 contains the highest mean concentrations: Fe (74.19 g/kg), Pb (831 mg/kg), Zn (2528 mg/kg), Cu (84 mg/kg), Cd (13 mg/kg), V (178 mg/kg), Mo (3 mg/kg) and Cr (122 mg/kg). The lowest mean concentrations of Fe (40 g/kg), Pb (58 mg/kg),
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Table 1 Summary of heavy metals in river sediments from four different sections. Heavy metals
Fe (g/kg) Pb (mg/kg) Zn (mg/kg) Cu (mg/kg) Ni (mg/kg) V (mg/kg) Co (mg/kg) Rb (mg/kg) Mo (mg/kg) Cd (mg/kg) Cs (mg/kg) Ba (mg/kg) Nd (mg/kg) Be (mg/kg) Cr (mg/kg) PLI
Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean
Sd Sd Sd Sd Sd Sd Sd Sd Sd Sd Sd Sd Sd Sd Sd Sd
LDR3 (upstream) (20)
LDR11 (middle stream) (19)
LDR14 (middle stream) (13)
LDR18 (downstream) (12)
All (64)
34.06e44.97 40.08 2.72 46e73 58 8 199e400 284 52 42e66 50 6 100e304 148 47 98e146 115 11 51e103 73 13 72e99 88 8 1.3e2.2 1.7 0.2 1.9e25 7.4 5.6 7.7e13.0 10.4 1.4 361e808 464 102 44e61 53 5 1.9e4.2 2.9 0.7 65e86 73 6 1.85e2.58 2.22 0.21
62.31e92.62 74.19 7.97 360e1735 831 450 958e5349 2528 1461 63e99 84 9 57e100 76 11 109e282 178 52 21e70 38 11 60e85 73 7 2.1e3.4 2.8 0.4 7.0e23.7 13.0 3.7 10.5e17.4 13.8 2.1 306e464 394 44 42e73 50 7 2.34e7.3 4.2 1.0 80e160 121 22 3.20e4.06 3.46 0.24
56.67e68.91 61.42 4.39 246e821 546 183 742e2182 1613 468 63e99 79 12 61e94 79 9 97e139 118 13 33e56 45 7 74e119 91 11 1.7e2.6 2.0 0.3 4.52e14.60 9.73 3.26 12.4e19.8 16.2 2.5 355e575 413 67 40e64 50 7 3.1e6.4 4.2 0.8 71e100 85 7 2.45e3.74 3.14 0.40
43.84e55.96 50.96 4.15 95e294 194 59 426e928 704 147 61e88 75 9 70e120 93.65 16.93 120e158 137 12 30e88 60 17 92e136 112 12 1.2e2.9 2.1 0.6 2.50e6.82 5.11 1.33 10.9e21.5 16.9 3.4 270e486 382 62 51e70 60 6 3.7e6.7 4.9 0.9 80e124 99 13 2.19e3.18 2.84 0.31
34.06e92.62 56.58 14.76 46e1735 412 413 199e5349 1299 1236 42e99 71 17 57e304 102 42 97e282 138 40 21e103 55 19 60e136 89 16 1.2e3.4 2.2 0.6 1.85e25.20 9.11 4.92 7.7e21.5 13.8 3.4 270e808 418 79 40e73 53 7 1.9e7.3 3.9 1.1 65e160 95 24 1.85e4.06 2.89 0.58
Zn (284 mg/kg), Cu (50 mg/kg), V (115 mg/kg), Mo (1.7 mg/kg) and Cr (73 mg/kg) are present in the upstream section LDR3. Mean concentrations of Ni, Co and Ba in LDR3 are higher than in other sampling sites (Table 1). The highest mean concentrations of other heavy metals (Rb, Cs, Nd, Be) in Table 1 are present in LDR18. The lateral and vertical changes of typical heavy metals (Fe, Pb, Zn, Cu, Ba, Nd) along the Lianshui River, with sufficient potential to reveal possible pollution sources, are given in Fig. 7 and Fig. 8, respectively. In upstream (LDR3) to downstream (LDR18), concentrations of Fe, Pb, Zn and Cu are observed in the following order: LDR11 > LDR14 > LDR18 > LDR3, however, obvious differences are not found in concentrations of Ba and Nd (Fig. 7). Concentrations of Fe, Pb, Zn in the vertical profiles show the same order with lateral characteristics when depths of less than 20 cm are considered. However, two high concentration peaks of Pb and Zn are found at w6 cm and w12 cm in the sediment profile LDR11; when depths >20 cm are considered, concentrations of Pb and Zn are more stable in LDR11 although they are still higher than those in LDR3 (Fig. 8aec). From the top to 22 cm, concentrations of Cu in LDR11, LDR14 and LDR18 are similar to each other and are all higher than in LDR3, increasing gradually with depth (Fig. 8d). In contrast, concentrations of Ba and Nb in different sampling sites seem to be more stable and are essentially independent of the depth (Fig. 8e,f). The pollution load index (PLI) proposed by Tomlinson et al. (1980) has been used obtained from concentration factors CF ¼ Cmetal/Cbackgroud value for selected metals. The PLI is then calculated by the n-root from the product of the n CFs of the metals included: PLI ¼ nO(CF1 CF2 CF3 .CFn) (Angulo,
1996); the lowest concentration value for each element was used as background value Cbackgroud value. According to Singh et al. (2003) PLI values vary from 0 (unpolluted) to 10 (highly polluted) as follows: PLI ¼ 0 background concentration; 0 < PLI 1 unpolluted; 1 < PLI 2 moderately to unpolluted; 2 < PLI 3 moderately polluted; 3 < PLI 4 moderately to highly polluted; 4 < PLI 5 highly polluted; PLI > 5 very highly polluted. As shown in Table 1, including all 15 heavy metals, the highest mean value is 3.46 in LDR11, followed by LDR14 (3.14), LDR18 (2.84) and LDR3 (2.22). A more detailed discussion of the PLI index is given in section 4.
3.4. Correlations of heavy metal concentrations and magnetic parameters Pearson’s correlation coefficients (P) between heavy metal concentrations and magnetic parameters are listed in Table 2. Ms, c, SIRM and cARM have significant positive correlations with V, Cr, Cu, Zn, Mo, Cd, Pb, Fe and PLI (of all 15 metals). They show significant negative correlation with Co, Ni, Rb and Nd. However, no significant correlations between magnetic parameters and Be as well as Cs are found; c and SIRM correlate negatively with Ba (Table 2). Since the P between SIRM and heavy metals are highest among all the magnetic parameters, we select SIRM to quantify the relationship between magnetic parameters and heavy metals. The scatter plots of SIRM versus Fe, Pb, Zn, Cu, Ba and Nd are given in Fig. 9 including the coefficient of determination R2 values. All heavy metals except Ba (R2 ¼ 0.10) and Nd (R2 ¼ 0.09) show a good correlation with SIRM i.e., Fe (R2 ¼ 0.90), Pb (R2 ¼ 0.88), Zn (R2 ¼ 0.86), Cu
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Fig. 7. Box plots of (a) Fe, (b) Pb, (c) Zn, (d) Cu, (e) Ba and (f) Nb for all samples from LDR3, LDR11, LDR14 and LDR18.
(R2 ¼ 0.71). The differences in the correlations between magnetic parameters and heavy metals are likely dependent on their sources. Principal component analysis (PCA) was applied to identify the source of heavy metals in the river sediments by applying Varimax rotation with Kaiser normalization (Wu, 2004). The results show that there are four factors that explain 85.6% of the total variance in the river sediments (Table 3). As shown in Table 3, the first factor explains 34.9% of the total variance and is dominated by Ms, c, SIRM, cARM, V, Cr, Mo and Fe; Factor 2 accounts for 26.4% of the total variance and loads in Cu, Zn, Cd and Pb; Factor 3 is dominated by Be, Rb, Cs and Nd, and accounts for 14.8% of the total variance; Factor 4 is dominated by Co, Ni and Ba and accounts for 9.5% of the total variance. The relationships between the heavy metals and the magnetic parameters based on the first three principle components of the sediments are illustrated in Fig. 10a in 3D space. Cluster analysis (CA) was performed to evaluate further the similarities of the heavy metal sources by applying Ward’s method (Wu, 2004). The CA results for magnetic parameters and heavy metals are shown in Fig. 10b as a dendrogram. Fig. 10b displays four clusters: (I) c, SIRM, Ms, Fe, cARM, V, Cr, Mo; (II) Zn, Pb, Cd; (III) Cu; (IV) Be, Cs, Rb, Nd, Co, Ni, Ba. As shown in this figure the clusters I, II and III join together at a relatively high level, likely implying anthropogenic activities, however, they differ from cluster IV. The CA results are in agreement with the PCA results listed in Table 3 and illustrated in Fig. 10a.
4. Discussion 4.1. Sources of magnetic minerals and heavy metals Magnetic, SEM and EDX results (Figs. 3e6) reveal that PSDeMD magnetite often occurring as spherules is the dominant ferrimagnetic phase with small amounts of SP magnetic particles in the middle stream and downstream sediments (at depth 0.4 are shown in bold). Components
Ms
c
SIRM
cARM Be V Cr Co Ni Cu Zn Rb Mo Cd Cs Ba Nd Pb Fe Eigenvalues % of Variance Cumulative %
1
2
3
4
0.82 0.80 0.7 0.83 0.30 0.94 0.90 0.41 0.31 0.57 0.25 0.37 0.84 0.25 0.01 0.03 0.00 0.30 0.76 6.6 34.9 34.9
0.48 0.52 0.54 0.26 0.05 0.07 0.17 0.65 0.53 0.54 0.89 0.25 0.33 0.74 0.51 0.08 0.52 0.89 0.55 5.0 26.4 61.3
0.17 0.14 0.12 0.15 0.76 0.05 0.18 0.07 0.25 0.50 0.04 0.81 0.20 0.09 0.76 0.01 0.70 0.06 0.10 2.8 14.8 76.1
0.20 0.24 0.22 0.12 0.27 0.06 0.12 0.42 0.56 0.16 0.16 0.16 0.12 0.13 0.14 0.91 0.23 0.16 0.18 1.8 9.5 85.6
Extraction Method: Principal Component Analysis, Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 5 iterations.
The degree of heavy metal pollution is clearly outlined as an isoline plot of the PLIanthro values showing the data as a function of distance and depth (Fig. 12). PLIanthro values are in the range of 0e2 before the river reaches the city identifying the upstream section as unpolluted to moderately polluted. The PLIanthro values increase gradually from 2 to 5 after entering into the city indicating that the river is moderately to highly polluted by anthropogenic activity here. The PLIanthro reach values vary from 5 to 6 near and beyond the Fe-smelting plant and increase to almost 8 at a distance of w2.5 km (LDR11) downstream from the plant. It then again decreases gradually to w5 near to the city boundary (LDR15); this shows that the river is highly polluted in this city region. PLIanthro values of w5e2.5 are sustained within a distance of w7 km after the river leaves the city indicating that this downstream section of the river remains moderately to highly polluted. It is also interesting to note that the highest PLIanthro values are not found directly at the Fe-smelting plant (sites LDR7eLDR9), but are present in the bend of the river (LDR11) within a distance of w2.5 km to the plant; at the same time, the highest value appears at a depth of w10e20 cm and not at the surface (Fig. 12). This is likely to result from the change of the hydraulic flow conditions along this section of the channel: pollutants will be retained in suspension where the width of the river is w80 m and the course is relatively straight as it is at the plant. After site LDR10 where the river makes a strong bend and the width is expanding to w150 m (LDR11), the flow velocity decreases and the solid particles precipitate from the suspension and deposit by helical water flow. Due to rapid flow and three wastewater outlets only little sediment is present directly at the plant (with the exception of gravels in the vicinity of LDR7eLDR9). In contrast sediment accumulation is high after the strong river bend and long sediment cores (such as LDR11) can be recovered here. High flow velocitiy leads to strong fluvial abrasion at the boundary between the water and the riverbed which can remove and transport coarser materials like the large anthropogenic 9e140 mm spherules. This coarser
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Fig. 10. (a) PCA results of LDR3, LDR11, LDR14 and LDR18 samples in the three-dimensional space: plots of loading of the first three principal components (different symbols mean different cluster-groups); (b) Dendrogram results of all samples from Ward method of hierarchical cluster analysis for 15 elements and 4 magnetic parameters.
Fig. 11. The relationship between (a) SIRM and PLI, (b)SIRM and PLIanthro for all samples in LDR3, LDR11, LDR14 and LDR18. Equations and coefficient of determination (R2) between log(SIRM) and PLI, PLIanthro are listed on the Figure. PLIanthro refers to the Tomlinson PLI of Fe, V, Cr, Mo, Zn, Pb, Cd and Cu in river sediments originating from anthropogenic activities.
Fig. 12. Isoline plot of PLIanthro values as a function of distance and depth. PLIanthro ¼ 0: background concentration; 0 < PLIanthro 1: unpolluted; 1 < PLIanthro 2: moderately to unpolluted; 2 < PLIanthro 3: moderately polluted; 3 < PLIanthro 4: moderately to highly polluted; 4 < PLIanthro 5: highly polluted; PLIanthro > 5: very highly polluted (Singh et al., 2003).
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fraction then precipitates after the river bend where the flow velocity becomes lower, whilst the fine particles are transported further downstream. 5. Conclusions We have shown that the coexistence of magnetic particles and heavy metals with significant correlations between them, make it possible to use magnetic methods as a tool for assessment of heavy metal pollution in industrial regions by surface water runoff. Specifically we find that the enrichment of coarse PSDeMD magnetite particles is the main reason for enhancement of magnetic concentration parameters (Ms, c, SIRM, cARM) in the river section downstream of a Fe-smelting plant. Antrhopogenic spherules are found by SEM and most likely originate from the plant. The correlation of elevated c and SIRM values with heavy metal (Fe, V, Cr, Mo, Zn, Pb, Cd, Cu) concentrations from industrial activities (mainly the Fe-smelting plant) prove that magnetic concentration parameters can be used as proxies for the anthropogenic contribution of pollutants to the river sediments. In contrast, very weak correlations with Be, Cs, Rb, Nd, Co, Ni and Ba, indicate that these metals stem from soil in the catchment region. The Tomlinson pollution load index (PLI) shows significant correlations with magnetic concentration parameters; most notably PLIanthro (including Fe, V, Cr, Mo, Zn, Pb, Cd, Cu) shows a strong positive correlation with SIRM values and a linear relationship with log(SIRM) can be established. Therefore, for the studied river sediments at Loudi, it can be concluded that log(SIRM) mapping is best suitable for quantitative assessment of heavy metal pollution. The degree and range of Lianshui River sediment pollution is controlled by surface water transport and deposition by precipitation of solid particles from suspension. Most of earlier works on magnetic proxies have considered pollution of soil and sediments by atmospheric input of fly ash. Our results demonstrate that also for transport by surface water the use of environmental magnetic methods in conjunction with auxiliary geochemical analysis can provide a fast and non-destructive tool for assessment of heavy metals pollution. Acknowledgments Qingsong Liu and two reviewers (Erwin Appel and Eduard Petrovský) are gratefully acknowledged for their constructive suggestions. This work was financially supported by the National Nature Science Foundation of China (Grants 40804014, 20677059, 40525013 and 40821091). References Al-Khashman, O.A., 2004. Heavy metal distribution in dust, street dust and soils from the work place in Karak Industrial Estate, Jordan. Atmospheric Environment 38, 6803e6812. Angulo, E., 1996. The Tomlinson pollution load index applied to heavy metal “Mussel-Watch” data: a useful index to assess coastal pollution. The Science of the Total Environment 187, 19e56. Awofolu, O.R., Mbolekwa, Z., Mtshemla, V., Fatoki, O.S., 2005. Levels of trace metals in water and sediments from Tyume River and its effects on an irrigated farmland. International Journal of Environmental Science and Technology 7 (3), 435e446. Ba1anda, M., Wieche c, A., Kim, D., Ka˛ kol, Z., Koz1owski, A., Niedziela, P., Sabol, J., Tarnawski, Z., Honig, J.M., 2005. Magnetic AC susceptibility of stoichiometric and low zinc doped magnetite single crystals. European Physical Journal B 43, 201e212. Banerjee, A.D.K., 2003. Heavy metal levels and solid phase speciation in street dusts of Delhi, India. Environmental Pollution 123, 95e105. Bijaksana, S., Huliselan, E.K., 2010. Magnetic properties and heavy metal content of sanitary leachate sludge in two landfill sites near Bandung, Indonesia. Environmental Earth Science 60, 409e419.
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