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Anthropogenic magnetic particles and heavy metals in the road dust: Magnetic identification and its implications
Atmospheric Environment 44 (2010) 1175e1185
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Anthropogenic magnetic particles and heavy metals in the road dust: Magnetic identification and its implications Tao Yang a, Qingsheng Liu b, *, Haixia Li c, Qingli Zeng b, Lungsang Chan d a
Institute of Geophysics, China Earthquake Administration, Beijing 100081, China Department of Geophysics, China University of Geosciences, Wuhan 430074, China c Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China d Department of Earth Sciences, University of Hong Kong, Hong Kong, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 28 July 2009 Received in revised form 16 December 2009 Accepted 22 December 2009
Magnetic properties of road dusts in the East Lake area in Wuhan, China, were measured and compared with the results of heavy metal analyses in order to delineate the sources of pollutants. A total of ninetyseven dust samples were collected spatially from four segments with different traffic density and field settings from the roads encircling the lake. Thermomagnetic and hysteresis measurements revealed that the dominant magnetic carrier is coarse-grained magnetite. Correlations between magnetic parameters and element concentrations with traffic density and distances to the industrial region revealed that elements Cu, Ni and Fe mainly originate from vehicle traffic, which is also the major source of coarser magnetic particles (e.g., pseudo-single-domain/multi-domain (PSD/MD) grains), while element Pb and the smaller grains such as single-domain (SD) magnetic particles mainly originate from industrial emissions. The ratio between anhysteretic remanent magnetization and low-field magnetic susceptibility (ARM/clf) can be employed as an indirect indicator for Cu, Fe and Ni emissions resulting from vehicle traffic. Due to the intermixture of elements from different sources, the element concentrations are not conclusive about the pollution source. A linear correlation between magnetic concentration-related parameters (e.g., ARM and saturation isothermal remanent magnetization, SIRM) and the concentrations of major elements (e.g., Cu, Co, Fe, Mn, Ni and V) suggests that they can be used as a proxy for heavy metal pollution. Road dusts in four segments show different magnetic characteristics, indicating various influxes of anthropogenic magnetic materials from vehicle traffic and industrial plants due to the different traffic loads and field settings. These results suggest that magnetic measurements can serve as an efficient complementary tool for the routinely employed geochemical methods to map the heavy metal pollution and trace the sources of pollutants in the road dust. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Environmental magnetism Heavy metals Atmospheric particulate matter Road dust
1. Introduction Street dust, which is generally formed by several components including both natural materials (e.g., resuspended soil and weathered materials) and anthropogenic matters (e.g., industrial and vehicle-generated pollutants), and also a source of atmospheric particulate matter, house dust, and water run-off particulate matter, is reported to adversely effect human's health (Harrison and Yin, 2000). Therefore, good knowledge of sources and distributions, and of the processes that redound to dust accumulation and/or dispersal in urban environment, is significant for the design of costeffective environmental monitoring strategies, which is important for environmental protection, remediation and management.
* Corresponding author. Fax: þ86 27 6788 3251. E-mail address: [email protected] (Q. Liu). 1352-2310/$ e see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.12.028
A number of studies have reported that dusts from various industrial combustion processes contain a significant amount of ferrimagnetic components (minerals, and/or phase) (Querol et al., 1996; Goddu et al., 2004; Jordanova et al., 2006), being the cause of enhanced magnetizations of dust and topsoil, especially those in the urban areas and sites downwind of industrial centers (Hoffmann et al., 1999; Hanesch et al., 2003; Yang et al., 2007). In addition, vehicles generate Fe-rich particles by combustion of carbon hydrites, abrasion/corrosion of engine and/or vehicle body materials (Hoffmann et al., 1999; Goddu et al., 2004; Gautam et al., 2005; Kim et al., 2007). In particular, these magnetic minerals often have a causal link with heavy metals, such as Cu, Pb, Zn, Cd and Cr (Matzka and Maher, 1999; Goddu et al., 2004; Gautam et al., 2005) and with organic compounds (Xie et al., 1999; Shilton et al., 2005). Such associations can not only be used for the identification of the spreading of pollutants derived from vehicular and/or industrial emissions (Matzka and Maher, 1999; Hanesch et al., 2003; Spassov et al., 2004;
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Fig. 1. Schematic map of the study area with the sampling locations of road dust.
Davila et al., 2006), but also enable magnetic measurements to serve as complementary tool for the routinely employed geochemical methods (Hoffmann et al., 1999; Xie et al., 1999; Matzka and Maher, 1999; Shu et al., 2001; Muxworthy et al., 2001; Gautam et al., 2005; Kim et al., 2007, 2008, 2009; Xia et al., 2008; Yang et al., 2009), which are known to be time-consuming, tedious and expensive. Recently, a two-year magnetic monitoring of roadside dust in Seoul, Korea revealed that the major magnetic phase within the dust is magnetite-like material, with magnetic concentrations and particle sizes systematically seasonal fluctuated due to the seasonal influx variations of anthropogenic magnetic materials (Kim et al., 2007). A transmission electron microscope/energy-dispersive X-ray (TEM/EDX) observation of the dust particles on tree leaves in Hangzhou, China, indicated that these particles are mainly composed of near-spherical, plate and irregular agglomerate Ferich particles, Ca-rich, S-rich, and silicate particles (Lu et al., 2008). Comparison of the magnetic properties between the Asian dust deposits in Seoul, Korea and source region samples in China, argued
that the Asian dust had become contaminated during its transportation across the industrial areas in eastern China and western Korea (Kim et al., 2008). Magnetic studies on the roadside dust from Seoul, Korea for 13-months demonstrated that seasonal mapping using a mean apparent magnetic concentration (AMCFe3O4 ¼ [observed saturation magnetizations (Ms)/Ms of Fe3O4]) could be highly informative on the investigation of spatio-temporal pollution characteristics in urban areas (Kim et al., 2009). All of these previous works have demonstrated that magnetic investigation on road dust is best suited for an effective spatial and temporal pollution monitoring in wide urban areas. This paper reports a study of the magnetic properties and their association with heavy metals of road dusts collected spatially from four segments of the lakeshore roads encircling one of the major basin (with an area of 27.8 km2) of the East Lake in Wuhan, China, with the aim to delineate the source of pollutants, and to examine the applicability of magnetic measurements for mapping urban environments within such a small area.
Table 1 Description of sampling locations of road dusts. Location
Description
Sampling points
Donghu Hospital / Moshan Mountain (DHE)
Hills, sanatoriums and Moshan Park on the roadside (average traffic density: 13.3 vehicles min1) Both sides are lake, the closest segments to the upwind industrial areas (average traffic density: 12.3 vehicles min1) Resident area on the roadside, heavy traffic (average traffic density: 32.6 vehicles min1) Campus and government on the roadside (average traffic density: 24 vehicles min1)
DHE-01eDHE-13 (“C”, n ¼ 13)
Liyuan Square / Moshan Mountain (DHN) Zhongnan Hospital / Liyuan Square (DHW) Zhongnan Hospital / Donghu Hospital (DHS)
DHN-01eDHN-38 (“-”, n ¼ 38) DHW-01eDHW-16 (“:”, n ¼ 16) DHS-01eDHS-13 (“A”, n ¼ 30)
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Table 2 Summary of magnetic parameters of road dusts. DHE (n ¼ 13)
DHN (n ¼ 38)
DHW (n ¼ 16)
DHS (n ¼ 30)
All (n ¼ 97)
clf (108 m3 kg1)
Range Mean SD
52.59e212.93 109.32 47.16
67.24e293.53 162.60 57.39
75.86e265.09 148.52 51.23
44.40e198.28 93.23 40.01
44.40e293.53 131.68 58.28
cfd%
Range Mean SD
0.3e1.8 0.9 0.46
0.0e5.0 0.9 0.96
0.0e1.9 1.1 0.50
0.3e3.1 1.1 0.65
0.0e5.0 1.0 0.75
ARM (105 Am2 kg1)
Range Mean SD
33.60e90.83 60.93 18.99
32.47e139.61 80.92 24.65
27.51e75.57 45.37 11.84
17.79e77.88 46.20 13.36
17.79e139.61 61.64 24.92
SIRM (103 Am2 kg1)
Range Mean SD
56.48e197.28 112.91 43.51
64.74e300.21 153.59 60.06
48.05e172.83 109.34 38.99
43.50e161.51 82.92 27.73
43.50e300.21 118.98 54.75
Magnetic parameters
2. Materials and methods 2.1. Study area and samples Wuhan, located in the middle reaches of the Yangtze River, is the capital of Hubei Province and the largest city in central China. Its population is approximately 8.18 million in the end of 2006, approximately 4.3 million of them reside in nine urban core districts within an area of 201 km2. The climate of the area is humid sub-tropical with an average annual temperature of 15.8e17.5 C and annual rainfall of 1269 mm. The prevailing wind direction is towards northwest in summer and southeast in winter. The East Lake, located in the northeast of Wuhan (Fig. 1), is the largest city lake in China. A number of roadway dikes have been constructed across the lake, dividing it into several water bodies; Guozheng and Tanglin Lakes are the two major basins. The Wuhan Iron and Steel Company (WISC), the third largest iron and steel consortium in China, and the Qingshan Thermal Power Plant (QTPP) that is a coalburning plant and has a total installed capacity of 986,000 kW, are
situated northeast of the East Lake. A heavy industrial area consisting of iron and steel processing, cement production, foundries, and machinery factories and coking plants, etc., is located to the north of the lake (Fig. 1). Road dust samples were collected from four segments of the Donghu Road encircling the Guozheng Lake over a 7-day dry weather period in September 2002. Sampling locations are shown in Fig. 1 and the details are given in Table 1. At each sampling site, road dust was collected by ground sweeping with a small paint brush from a square of 1e2 m2, and transferred to clean, self-sealed polyethylene bags. Totally, ninety-seven dust samples were obtained. In the laboratory, all samples were air-dried at room temperature and passed through a 1-mm sieve to remove refuse and small stones. 2.2. Magnetic measurements Magnetic low-field susceptibilities were measured at two frequencies (0.47 kHz and 4.7 kHz) using a Bartington Instruments
Fig. 2. Box plot of low-field magnetic susceptibility (clf), frequency-dependent magnetic susceptibility (cfd%), ARM, and SIRM for road dust. The boxes in the box plot give the interquartile range of the values, the lines in the boxes the median values.
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MS2B sensor. The values are expressed as mass-specific susceptibility clf and chf, respectively. Frequency-dependent susceptibility was then calculated and expressed as a percentage cfd% ¼ (clf chf)/ clf 100%. An anhysteretic remanent magnetization (ARM) was imparted using a peak alternating field (AF) of 100 mT with a direct current (DC) bias field of 0.05 mT parallel to the AF, and was measured using a Molspin magnetometer. Isothermal remanent magnetization (IRM) measurements were carried out using an ASC Scientific model IM-10 impulse magnetizer and Molspin magnetometer. The IRM acquired in a peak field of 1 T was regarded as saturation IRM (SIRM). In addition, a number of representative
samples were magnetized in stepwise increasing DC fields to obtain IRM acquisition curves, 12 steps from 7 to 1100 mT were used. Magnetic hysteresis loops for representative subset of samples were measured using a Princeton Alternating Gradient Force Magnetometer (Model 2900 AGM) at the Institute of Rock Magnetism, University of Minnesota, USA, the maximum applied field was 1.0 T. Hysteresis parameters, saturation magnetization (Ms), saturation remanence (Mrs) and coercive force (Bc) were calculated after paramagnetic slope correction. To obtain the remanence coercivity (Bcr), after reaching 1.0 T, samples were remagnetized using backward field method.
Fig. 3. Isothermal remanent magnetization (IRM) acquisition and back-field demagnetization curves for selected road dust samples.
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Temperature dependence of the magnetic low-field susceptibility was determined using a Bartington Instruments MS2 system with a high temperature attachment in air. Measurements were performed from room temperature up to 700 C, with a measurement interval of 2 C and a heating and cooling rate of 5 C min1 and 10 C min1, respectively. 2.3. Heavy metal analyses About 3 g of road dust mixed with low-pressure polyethylene powder is pressed to obtain a powder pellet with diameter of ca. 34 mm. The analyses for major elements was performed on the pellet using a Siemens SRS303 X-ray fluorescence (XRF) spectrometer equipped with a Rh-anode X-ray tube and the Siemens SPECTRA AT evaluation software at the State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences. The analytical accuracy was <5% and precision was <2%. 3. Results 3.1. Magnetic properties The magnetic parameters of road dusts are summarized in Table 2. The clf of road dusts in DHN and DHW segments are generally higher than those in DHE and DHS (Table 2 and Fig. 2). The average clf values of road dusts from DHN, DHW, DHE and DHS are 162.60, 148.52, 109.32, and 93.23 108 m3 kg1, respectively.
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The cfd% values of road dusts range from 0.0% to 5.0% with mean values of w1%. ARM and SIRM of road dusts peak at DHN. The mean value of ARM is the lowest in DHW, whilst the mean value of SIRM is the lowest in DHS (Fig. 2c, d). IRM acquisition and back-field demagnetization curves for representative samples are shown in Fig. 3. IRM acquisition curves rapidly reach saturation below 300 mT, regardless of sampling locations; whilst back-field demagnetization curves display much softer behaviour, with Bcr lower than 30 mT. All hysteresis loops are thin, closed and approach magnetic saturation in the field of 300 mT (Fig. 4), indicating the magnetic minerals are dominated by low coercivity ferrimagnetic minerals. This finding can be confirmed by the ceT curves (Fig. 5). For road dusts from DHE, DHN and DHS segments, heating runs show the presence of several magnetic phases or phase transitions (Fig. 5a): The increased susceptibility above ca. 200 C may be due to phase transition (Kontny et al., 2000); The susceptibility increased gradually up to ca. 460 C, which might represent the Hopkinson peak on heating, and subsequently decreased up to 580 C, revealing the presence of Fe3O4 as the dominant magnetic carrier. For those from DHW segment (Fig. 5b), the Tc of 580 C confirms the present of magnetite (Dunlop and Özdemir, 1997). The cooling curve shows a significant decrease in susceptibility (Fig. 5b) could be due to that some foreign atoms diffuse into the crystal lattice or the alterations of magnetite-like materials to unidentified non-ferrimagnetic materials during heating, and hence lowering the magnetization and the Tc.
Fig. 4. Magnetic hysteresis loops for representative road dust samples.
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V and Cr are the lowest. In DHN segment, mean concentrations of Fe, V, Pb, Ni, Mn, Cr and Co are the highest (Table 3). Mean concentration of Zn is the highest in segment DHE (Fig. 6c), where the mean concentrations of Pb, Mn, Cr and Co follow those in DHN. Mean concentrations of Fe and Cu in segment DHS are the lowest. The Tomlinson pollution load index (PLI) (Angulo, 1996) has been often used to assess the relative heavy metal toxicity and how much a sample exceeds the element concentrations for natural environments. The PLI index is defined as the nth root of the multiplication of the concentration factors (CFHM), where CFHM is the ratio between the concentration of each heavy metal (CHM) to its corresponding background value (Cbackground) or the lowest concentration value detected for each heavy metal (Clowest). In the present study, the lowest concentration value for each element (Clowest) in the whole set of 31 samples was used to calculate the PLI index.
CFHM ¼ CHM =Clowest ;
(1)
and
PLI ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi p n CFHM1 CFHM2 . CFHMn :
(2)
Table 3 shows that the PLI in DHN ranges from 1.23 to 2.08 with the highest mean value (up to 1.69), and followed by DHE (1.51), DHW (1.38) and DHS (1.36). 3.3. Associations of heavy metals with magnetic parameters
Fig. 5. Representative types of temperature-dependent magnetic susceptibility curves. Each curve was normalized with its corresponding magnetic susceptibility at room temperature cRT. The black and grey lines denote heating and cooling runs, respectively.
3.2. Heavy metal concentrations Heavy metal concentrations are given in Table 3 and the comparison of concentrations of Fe, Cu, Pb, Zn, Mn and Co are shown in Fig. 6. The highest mean concentration of Cu is present in DHW segment (Fig. 6b), where mean concentrations of Pb, Mn,
Table 4 lists the Pearson's correlation coefficients between element concentrations and magnetic parameters. The correlation matrix indicates that clf significantly correlates with Fe, Cu and Co, however, ARM, and SIRM have a much stronger correlation with all the heavy metals except for Pb and Zn that has a very weak negative correlation for all magnetic parameters (Table 4). The differences in the correlations between magnetic parameters and heavy metals possibly due to the dependence of magnetic concentration-related parameters on mineral phase and grain size, and/or to the intermixture of elements from different sources and their association with different magnetic minerals, and also to various physical and chemical effects that occurred during their transportation and after deposition.
Table 3 Heavy metal concentrations and PLI for road dusts. DHE (n ¼ 5)
DHN (n ¼ 11)
DHW (n ¼ 6)
DHS (n ¼ 9)
All (n ¼ 31)
Fe (%)
Range Mean SD
2.81e3.13 2.94 0.12
2.22e5.35 3.87 1.06
2.59e3.56 3.07 0.41
1.97e3.16 2.66 0.42
1.97e5.35 3.21 0.85
Zn (mg kg1)
Range Mean SD
176e260 238.2 35.0
160e299 215.9 43.5
197e260 218.5 23.4
136e355 230.3 68.0
136e355 224.2 46.8
V (mg kg1)
Range Mean SD
55.2e71.7 62.6 5.9
58.7e90.3 71.8 10.3
52.0e63.8 58.2 3.8
51.0e76.4 64.5e8.1
51.0e90.3 65.6 9.3
Pb (mg kg1)
Range Mean SD
92.6e124.0 109.2 13.3
92.7e265.0 134.3 54.3
60.3e79.7 68.0 6.7
63.0e113.0 83.1 16.0
60.3e265.0 102.6 42.5
Ni (mg kg1)
Range Mean SD
19.8e28.3 24.7 3.61
24.7e45.7 31.0 7.1
23.0e32.3 27.4 3.1
21.2e38.0 25.7 5.4
19.8e45.7 27.7 5.9
Mn (mg kg1)
Range Mean SD
585e616 604.6 11.8
515e1123 686.3 163.2
478e587 538.7 37.7
439e836 542.8 118.2
439e1123 602.9 131.6
Cr (mg kg1)
Range Mean SD
68.8e86.1 79.0 7.1
68.7e103.0 82.4 12.6
58.9e74.7 66.0 6.4
54.4e96.2 70.7 12.3
54.4e103.0 75.3 12.3
Cu (mg kg1)
Range Mean SD
52.9e70.4 61.8 7.5
40.5e125.0 64.3 24.4
61.2e87.1 72.0 9.4
37.7e75.4 53.2 13.4
37.7e125.0 62.1 17.7
Co (mg kg1)
Range Mean SD
10.7e13.2 12.2 1.1
7.7e41.4 15.5 9.2
8.6e11.6 10.1 1.2
6.9e13.3 10.1 2.5
6.9e41.4 12.4 6.0
PLI
Range Mean SD
1.31e1.63 1.51 0.13
1.23e2.08 1.69 0.28
1.29e1.52 1.38 0.08
1.09e1.73 1.36 0.24
1.09e2.08 1.51 0.26
Heavy metals
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Fig. 6. Comparison of heavy metal concentrations of road dust in four segments. The boxes in the box plot give the inter-quartile range of the values, the lines in the boxes the median values.
4. Discussion 4.1. Magnetic mineralogy and granulometry The integrated magnetic results (Figs. 3e5) reveal that magnetite is the dominant ferrimagnetic phase in the road dust. cfd% is
sensitive to the superparamagnetic (SP) component, if cfd% > 4%, the assemblage of magnetic grains contains a significant portion of SP particles; in the case of cfd% < 4%, the proportion of SP particles is low (Dearing et al., 1996). Accordingly, magnetic carriers in the road dusts (with mean cfd% values of w1.0%) are predominately coarse-grained particles and the proportion of SP particles is much
Table 4 Correlation coefficients between heavy metals concentration, PLI and magnetic parameters for road dusts (n ¼ 31).
clf cfd% ARM SIRM a b
Fe
Zn
V
Pb
Ni
Mn
Cr
Cu
Co
PLI
0.740a 0.491a 0.779a 0.824a
0.106 0.290 0.059 0.072
0.278 0.331 0.441b 0.385b
0.035 0.343 0.316 0.204
0.351 0.291 0.423b 0.407b
0.349 0.298 0.552a 0.485a
0.234 0.283 0.387b 0.340
0.486a 0.474a 0.369b 0.504a
0.366b 0.362b 0.542a 0.494a
0.450b 0.531a 0.623a 0.595a
Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed).
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low. It is further confirmed by the Day plot (Day et al., 1977) in which the ratios of Mrs/Ms and Bcr/Bc occupy the single-domain and pseudo-single-domain (SD/PSD) regions (Fig. 7). A dimensionless ratio between anhysteretic susceptibility and low-field susceptibility (cARM/clf) can indicate changes in magnetite grain size if the magnetic carrier is dominantly magnetite and significant amounts of very fine SP grains are absent; a higher slope (cARM/clf) indicates a smaller magnetic grain size, because cARM is sensitive to the stable single-domain (SSD) and fine PSD ferrimagnetic grains (King et al., 1982; Evans and Heller, 2003). In Fig. 8, samples from segment DHW show relatively lower cARM/clf slopes, suggesting the presence of coarser-grained magnetite, whilst the higher cARM/clf slope for segments DHN and DHE indicate a relative smaller magnetite grain size. 4.2. Anthropogenic magnetic particles and heavy metals in the road dust Road dust is a mixture of natural and anthropogenic components, both contain magnetic mineral fractions with specific magnetic properties. Together the high values of clf and SIRM, low cfd%, and the presence of coarse-grained magnetite as the primary magnetic carrier, it reveals that magnetic properties of the road dusts are dominated by ferrimagnetic minerals with anthropogenic origins (Fialova et al., 2006; Magiera et al., 2006). In order to check the dependence of magnetic parameter and element concentrations on the traffic density or the distance to the industrial region, the values of them for 11 samples with traffic record were normalized in the following way, to obtain dimensionless values:
xnorm ¼
x xmin xmax xmin
(3)
A complicated behaviour is seen when plotting clf, ARM and SIRM against traffic density (Fig. 9a). Low values are found in places
Fig. 7. Day plot of the ratios Mrs/Ms and Bcr/Bc of road dust. Single-domain (SD), pseudo-single-domain (PSD) and multi-domain (MD) boundaries are after Dunlop (2002).
Fig. 8. Bivariate plot of anhysteretic susceptibility (cARM) versus low-field susceptibility (clf) of road dust (King et al., 1982). Numbers in parentheses in the legend are the average traffic densities in corresponding segment. Samples from the low traffic density segments DHE and DHN contain finer grains than the heavy traffic sites DHS and DHW.
with intermediate traffic densities. All three magnetic parameters behave similar. The grain size parameter ARM/clf shows a significant (at 1%) anticorrelation with increasing traffic (Fig. 9b). The ARM/clf ratio is low at high traffic density. An increased number of vehicles rises thus the contribution of larger grains to susceptibility and the ratio ARM/clf falls. Vehicle traffic emissions can hence be associated with larger grain sizes (see also Fig. 8). Most of the element concentrations do not show simple linear trends. This may be due to the presence of a second major pollution source, i.e. the industrial region where the WISC and QTPP are situated. However, iron, nickel and copper concentrations are generally higher at higher traffic densities. The significant positive correlation was found between Cu and traffic density (Fig. 9c). The above mentioned ARM/clf is hence an indirect indicator for Cu, Fe and Ni emissions resulting from vehicle traffic being related to coarser magnetic minerals. The significant correlation (at 5%) between traffic density and distance to the industrial region (Fig. 10a) indicates that the places close to the industrial region have relative low traffic density, and vice versa. Consequently, the contribution of magnetic particles and elements from the industrial emissions should be generally decreased, and that from vehicle traffic should be increased with the increasing distance to the industrial region. The intermixture of magnetic particles from these sources could hence be the reason for the double peaks in clf, SIRM and ARM at low and high traffic densities, respectively (Fig. 9a). However, for clf and SIRM, magnetic particles from both, vehicle traffic and industrial emissions contribute to the corresponding bulk values. This is not the case for the ARM which is more selective because MD and PSD grains have smaller contributions to it. Thus one sees mainly finer particle (e.g., SD grains) contributions which are apparently related to longdistance transported fly ashes and/or emissions originated from WISC and QTPP. This can be supported by the significant negative correlation (at 5%) between ARM/clf and the distance to the industrial region (Fig. 10b). If copper concentrations are mainly associated with traffic, they should be independent from the distance to the industrial region.
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Fig. 9. (a) Normalized values (cf. equation (3)) of clf, ARM and SIRM in function of number of vehicles. (b) A significant negative correlation is found between the magnetic grain size parameter ARM/clf and traffic density, while copper concentrations show a significant positive correlation with the amount of traffic (c).
In order to test this hypothesis, Cu was plotted against the distance to the industrial region. Indeed, no significant correlation with was found (Fig. 10c). In contrast, the lead concentration shows a significant (at 5%) anticorrelation with increasing distance to the industrial region (Fig. 10d), indicating that this element being present in the road dust mainly originates from industrial emissions. All other elements have no significant correlation with the distance to the industrial region. It may be caused by the intermixture of elements from different sources, and proposes that the element concentrations are not conclusive about the pollution source. 4.3. Environmental implications of magnetic characteristics of road dust To sum up, it is found that road dusts from four segments have diverse magnetic characteristics, for example, the different slopes of cARM/clf (Fig. 8), and different thermomagnetic alterations (Fig. 5). It seems that these diversities result from the different composition, grain size and concentrations of the magnetic carrier in road dusts; however, they are essentially indicative of the different sources of magnetic particles in the road dusts as discussed above. Most magnetic particles derived from vehicle emissions have been identified as a non-spherical aggregate of iron-oxides (mainly magnetite) and FeeCeS materials (Matzka and Maher, 1999; Abdul-Razzaq and Gautam, 2001; Maher et al., 2008; Lu et al., 2008), whilst, for those produced from the abrasion or corrosion of
the vehicle engine and body work, they were found in the form of an aggregate of pure Fe and AleCaeFeeKeMgeSi materials (Kim et al., 2007). However, most of the magnetic fraction (mainly magnetite/maghemite) in coal-fired fly ash is present in spherules with diameters of 2e50 mm (Strzyszcz et al., 1996; Sokol et al., 2000). Furthermore, vehicle emissions are dominated by ferrimagnetic particles whereas the fly ashes are magnetically harder due to the presence of hematite (Lecoanet et al., 2003). These differences in magnetic minerals, grain size and morphology should be responsible for the diverse magnetic properties of the road dusts from these four segments (Figs. 5 and 8), and the different relations between magnetic parameters and heavy metal concentrations with the traffic density and distances to the industrial region (Figs. 9 and 10). These results make it possible to discriminate the sources from industrial plants and road traffic using magnetic methods (Hoffmann et al., 1999; Shu et al., 2001; Lecoanet et al., 2003; Spassov et al., 2004), although the exact mechanism that led to these magnetic differences has not been fully understood. Almost all the heavy metals (except Pb and Zn) show good correlation with the magnetic remanence parameters ARM and SIRM (Table 4). These associations could be an indicative of an anthropogenic nature of the magnetic carrier, revealing that magnetic particles and heavy metals coexist in the road dusts. Among them, Fe, Mn, and Co show much strong correlation with ARM, followed by V, Ni, Cr, and Cu, suggesting that ARM is a more efficient indicator for dust heavy metals. It is also interesting to note that the Tomlinson PLI significantly correlates with magnetic
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Fig. 10. (a) Test for correlation between traffic density and distance to the industrial region for the different sampling locations, significant correlation is seen at a 5% confidence level. ARM/clf ratio (b) shows a significant correlation with the distance to the industrial region, but not for the element Copper (c). In contrast, the Lead (d) shows a significant negative correlation with the distance to the industrial area.
concentration-related parameters clf, ARM, and SIRM (Table 4), indicating that magnetic concentration is more proportional to the concentration of collective heavy metals rather than each individual concentration, and that the comprehensive evaluation of heavy metals is more reasonable. Regardless of the nature for coexistence of the heavy metals and magnetic particles in road dusts, these significant correlations make it is possible to employ magnetic measurements as a tool for mapping dust pollutions, at least in the studied area.
not conclusive about the pollution source. Significant correlations between the concentrations of the most heavy metals (e.g., Cu, Co, Cr, Fe, Mn, Ni and V) and magnetic remanence parameter ARM, pointed out the potential of magnetic parameters for simple, rapid and non-destructive proxy indications of heavy metals, although the nature of the linkage between heavy metals and magnetic components is still an open problem. It also seems to be possible to identify and distinguish different pollution sources by magnetic measurements, together with the auxiliary geochemical studies.
5. Conclusions
Acknowledgements
Magnetic properties and their associations with heavy metal contents of the road dust in East Lake area in Wuhan, China were presented. The dominant magnetic carrier of road dust is coarsegrained magnetite. Correlations between magnetic parameters and element concentrations with traffic loads and distances to the industrial region revealed that coarser magnetic grains (e.g., PSD/ MD) and elements Fe, Cu, and Ni are mainly related to vehicle traffic, while finer grains (e.g., SD) and element Pb mainly originate from long-distance transported fly ashes/emissions released by the nearby industrial plants. ARM/clf can be used as an indirect indicator for Cu, Fe and Ni emissions resulting from vehicle traffic which related to coarser magnetic minerals. Due to the intermixture of elements from different sources, their concentrations are
This study was supported by the National Natural Science Foundation of China (No. 40474025). We express our deep gratitude to Dr. Simo Spassov and an anonymous referee whose comments and suggestions greatly improved the manuscript. The authors thank Dr. Qingsong Liu for help with hysteresis loop measurements. References Abdul-Razzaq, W., Gautam, M., 2001. Discovery of magnetite in the exhausted material from a diesel engine. Applied Physics Letters 78, 2018e2019. Angulo, E., 1996. The Tomlinson pollution load index applied to heavy metal “Mussel-Watch” data: a useful index to assess coastal pollution. Science of the Total Environment 187, 19e56.
T. Yang et al. / Atmospheric Environment 44 (2010) 1175e1185 Davila, A.F., Rey, D., Mohamed, K., Rubio, B., Guerra, A.P., 2006. Mapping the sources of urban dust in a coastal environment by measuring magnetic parameters of Platanus hispanica leaves. Environmental Science and Technology 40, 3922e3928. Day, R., Fuller, M., Schmidt, V.A., 1977. Hysteresis properties of titanomagnetites: grain-size and compositional dependence. Physics of the Earth and Planetary Interiors 13, 260e267. Dearing, J.A., Hay, K.L., Baban, S.M.J., Huddleston, A.S., Wellington, E.M.H., Loveland, P.J., 1996. Magnetic susceptibility of soil: an evaluation of conflicting theories using a national data set. Geophysical Journal International 127, 728e734. Dunlop, D.J., 2002. Theory and application of the Day plot (Mrs/Ms versus Hcr/Hc) 1. Theoretical curves and tests using titanomagnetite data. Journal of Geophysical Research 107 (B3), 2056. doi:10.1029/2001JB000486. Dunlop, D.J., Özdemir, Ö, 1997. Rock Magnetism: Fundamentals and Frontiers. Cambridge University Press, Cambridge, p. 573. Evans, M.E., Heller, F., 2003. Environmental Magnetism: Principles and Applications of Enviromagnetics. Academic Press, London, p. 299. Fialova, H., Maier, G., Petrovský, E., Kapi cka, A., Boyko, T., Scholger, R., MAGPROX Team, 2006. Magnetic properties of soils from sites with different geological and environmental settings. Journal of Applied Geophysics 59, 273e283. Gautam, P., Blaha, U., Appel, E., 2005. Magnetic susceptibility of dust-loaded leaves as a proxy of traffic-related heavy metal pollution in Kathmandu city, Nepal. Atmospheric Environment 39, 2201e2211. Goddu, S.R., Appel, E., Jordanova, D., Wehland, F., 2004. Magnetic properties of road dust from Visakhapatnam (India)erelationship to industrial pollution and road traffic. Physics and Chemistry of the Earth 29, 985e995. Hanesch, M., Scholger, R., Rey, D., 2003. Mapping dust distribution around an industrial site by measuring magnetic parameters of tree leaves. Atmospheric Environment 37, 5125e5133. Harrison, R.M., Yin, J., 2000. Particulate matter in the atmosphere: which particle properties are important for its effects on health? Science of the Total Environment 249, 85e101. Hoffmann, V., Knab, M., Apple, E., 1999. Magnetic susceptibility napping of roadside pollution. Journal of Geochemical Exploration 99, 313e326. Jordanova, D., Jordanova, N., Hoffmann, V., 2006. Magnetic mineralogy and grain-size dependence of hysteresis parameters of single spherules from industrial waste products. Physics of the Earth and Planetary Interiors 154, 255e265. Kim, W., Doh, S.J., Park, Y.H., Yun, S.T., 2007. Two-year magnetic monitoring in conjunction with geochemical and electron microscopic data of roadside dust in Seoul, Korea. Atmospheric Environment 41, 7627e7641. Kim, W., Doh, S.J., Yu, Y., 2009. Anthropogenic contribution of magnetic particulates in urban roadside dust. Atmospheric Environment 43, 3137e3144. Kim, W., Doh, S.J., Yu, Y., Lee, M., 2008. Role of Chinese wind-blown dust in enhancing environmental pollution in Metropolitan Seoul. Environmental Pollution 153, 333e341. King, J.W., Banerjee, S.K., Marvin, J., Özdemir, Ö, 1982. A comparison of different magnetic methods of determining the relative grain size of magnetite in natural materials: some results from lake sediments. Earth and Planetary Science Letters 59, 404e419.
1185
Kontny, A., de Wall, H., Sharp, T.G., Pósfai, M., 2000. Mineralogy and magnetic behavior of pyrrhotite from a 260 C section at the KTB drilling site, Germany. American Mineralogist 85, 1416e1427. Lecoanet, H., Léveque, F., Ambrosi, J.P., 2003. Combination of magnetic parameters: an efficient way to discriminate soil-contamination sources (south France). Environmental Pollution 122, 229e234. Lu, S.G., Zheng, Y.W., Bai, S.Q., 2008. A HRTEM/EDX approach to identification of the source of dust particles on urban tree leaves. Atmospheric Environment 42, 6431e6441. Maher, B.A., Moore, C., Matzka, J., 2008. Spatial variation in vehicle-derived metal pollution identified by magnetic and elemental analysis of roadside tree leaves. Atmospheric Environment 42, 364e373. Magiera, T., Strzyszcz, Z., Kapi cka, A., Petrovský, E., MAGPROX Team, 2006. Discrimination of lithogenic and anthropogenic influences on topsoil magnetic susceptibility in Central Europe. Geoderma 130, 299e311. Matzka, J., Maher, B.A., 1999. Magnetic biomonitoring of roadside tree leaves: identification of spatial and temporal variations in vehicle-derived particulates. Atmospheric Environment 33, 4565e4569. Muxworthy, A.R., Matzka, J., Petersen, N., 2001. Comparison of magnetic parameters of urban atmospheric particulate matter with pollution and meteorological data. Atmospheric Environment 35, 4379e4386. Querol, X., Alastuey, A.S., Lopez-Soler, A., Mantillai, E., Plana, F., 1996. Mineral composition of atmospheric particulates around a large coal-fired power station. Atmospheric Environment 30, 3557e3572. Shilton, V.F., Booth, C.A., Smith, J.P., Giess, P., Mitchell, D.J., Williams, C.D., 2005. Magnetic properties of urban street dust and their relationship with organic matter content in the West Midlands, UK. Atmospheric Environment 39, 3651e3659. Shu, J., Dearing, J.A., Morse, A.P., Yu, L., Yuan, N., 2001. Determining the sources of atmospheric particles in Shanghai, China, from magnetic and geochemical properties. Atmospheric Environment 35, 2615e2625. Sokol, E.V., Maksimova, N.V., Volkova, N., Nigmatulina, E., Frenkel, A., 2000. Hollow silicate microspheres from fly ashes of the Chelyabinsk brown coals (South Urals, Russia). Fuel Processing Technology 67, 35e52. Spassov, S., Egli, R., Heller, F., Nourgaliev, D.K., Hannam, J., 2004. Magnetic quantification of urban pollution sources in atmospheric particulate matter. Geophysical Journal International 159, 555e564. Strzyszcz, Z., Magiera, T., Heller, F., 1996. The influence of industrial emissions on the magnetic susceptibility of soils in Upper Silesia. Studia Geophysica et Geodetica 40, 276e286. Xia, D.S., Chen, F.H., Bloemendal, J., Liu, X.M., Yu, Y., Yang, L.P., 2008. Magnetic properties of urban dustfall in Lanzhou, China, and its environmental implications. Atmospheric Environment 42, 2198e2207. Xie, S., Dearing, J.A., Bloemendal, J., Boyle, J.F., 1999. Association between the organic matter content and magnetic properties in street dust, Liverpool, UK. Science of the Total Environment 241, 205e214. Yang, T., Liu, Q.S., Chan, L.S., Cao, G.D., 2007. Magnetic investigation of heavy metals contamination in urban topsoils around the East Lake, Wuhan city, China. Geophysical Journal International 171, 603e612. Yang, T., Liu, Q.S., Zeng, Q.L., Chan, L.S., 2009. Environmental magnetic responses of urbanization processes: evidence from lake sediments in East Lake, Wuhan, China. Geophysical Journal International 179, 873e886.
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Anthropogenic magnetic particles and heavy metals in the road dust: Magnetic identification and its implications Tao Yang a, Qingsheng Liu b, *, Haixia Li c, Qingli Zeng b, Lungsang Chan d a
Institute of Geophysics, China Earthquake Administration, Beijing 100081, China Department of Geophysics, China University of Geosciences, Wuhan 430074, China c Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China d Department of Earth Sciences, University of Hong Kong, Hong Kong, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 28 July 2009 Received in revised form 16 December 2009 Accepted 22 December 2009
Magnetic properties of road dusts in the East Lake area in Wuhan, China, were measured and compared with the results of heavy metal analyses in order to delineate the sources of pollutants. A total of ninetyseven dust samples were collected spatially from four segments with different traffic density and field settings from the roads encircling the lake. Thermomagnetic and hysteresis measurements revealed that the dominant magnetic carrier is coarse-grained magnetite. Correlations between magnetic parameters and element concentrations with traffic density and distances to the industrial region revealed that elements Cu, Ni and Fe mainly originate from vehicle traffic, which is also the major source of coarser magnetic particles (e.g., pseudo-single-domain/multi-domain (PSD/MD) grains), while element Pb and the smaller grains such as single-domain (SD) magnetic particles mainly originate from industrial emissions. The ratio between anhysteretic remanent magnetization and low-field magnetic susceptibility (ARM/clf) can be employed as an indirect indicator for Cu, Fe and Ni emissions resulting from vehicle traffic. Due to the intermixture of elements from different sources, the element concentrations are not conclusive about the pollution source. A linear correlation between magnetic concentration-related parameters (e.g., ARM and saturation isothermal remanent magnetization, SIRM) and the concentrations of major elements (e.g., Cu, Co, Fe, Mn, Ni and V) suggests that they can be used as a proxy for heavy metal pollution. Road dusts in four segments show different magnetic characteristics, indicating various influxes of anthropogenic magnetic materials from vehicle traffic and industrial plants due to the different traffic loads and field settings. These results suggest that magnetic measurements can serve as an efficient complementary tool for the routinely employed geochemical methods to map the heavy metal pollution and trace the sources of pollutants in the road dust. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Environmental magnetism Heavy metals Atmospheric particulate matter Road dust
1. Introduction Street dust, which is generally formed by several components including both natural materials (e.g., resuspended soil and weathered materials) and anthropogenic matters (e.g., industrial and vehicle-generated pollutants), and also a source of atmospheric particulate matter, house dust, and water run-off particulate matter, is reported to adversely effect human's health (Harrison and Yin, 2000). Therefore, good knowledge of sources and distributions, and of the processes that redound to dust accumulation and/or dispersal in urban environment, is significant for the design of costeffective environmental monitoring strategies, which is important for environmental protection, remediation and management.
* Corresponding author. Fax: þ86 27 6788 3251. E-mail address: [email protected] (Q. Liu). 1352-2310/$ e see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.12.028
A number of studies have reported that dusts from various industrial combustion processes contain a significant amount of ferrimagnetic components (minerals, and/or phase) (Querol et al., 1996; Goddu et al., 2004; Jordanova et al., 2006), being the cause of enhanced magnetizations of dust and topsoil, especially those in the urban areas and sites downwind of industrial centers (Hoffmann et al., 1999; Hanesch et al., 2003; Yang et al., 2007). In addition, vehicles generate Fe-rich particles by combustion of carbon hydrites, abrasion/corrosion of engine and/or vehicle body materials (Hoffmann et al., 1999; Goddu et al., 2004; Gautam et al., 2005; Kim et al., 2007). In particular, these magnetic minerals often have a causal link with heavy metals, such as Cu, Pb, Zn, Cd and Cr (Matzka and Maher, 1999; Goddu et al., 2004; Gautam et al., 2005) and with organic compounds (Xie et al., 1999; Shilton et al., 2005). Such associations can not only be used for the identification of the spreading of pollutants derived from vehicular and/or industrial emissions (Matzka and Maher, 1999; Hanesch et al., 2003; Spassov et al., 2004;
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Fig. 1. Schematic map of the study area with the sampling locations of road dust.
Davila et al., 2006), but also enable magnetic measurements to serve as complementary tool for the routinely employed geochemical methods (Hoffmann et al., 1999; Xie et al., 1999; Matzka and Maher, 1999; Shu et al., 2001; Muxworthy et al., 2001; Gautam et al., 2005; Kim et al., 2007, 2008, 2009; Xia et al., 2008; Yang et al., 2009), which are known to be time-consuming, tedious and expensive. Recently, a two-year magnetic monitoring of roadside dust in Seoul, Korea revealed that the major magnetic phase within the dust is magnetite-like material, with magnetic concentrations and particle sizes systematically seasonal fluctuated due to the seasonal influx variations of anthropogenic magnetic materials (Kim et al., 2007). A transmission electron microscope/energy-dispersive X-ray (TEM/EDX) observation of the dust particles on tree leaves in Hangzhou, China, indicated that these particles are mainly composed of near-spherical, plate and irregular agglomerate Ferich particles, Ca-rich, S-rich, and silicate particles (Lu et al., 2008). Comparison of the magnetic properties between the Asian dust deposits in Seoul, Korea and source region samples in China, argued
that the Asian dust had become contaminated during its transportation across the industrial areas in eastern China and western Korea (Kim et al., 2008). Magnetic studies on the roadside dust from Seoul, Korea for 13-months demonstrated that seasonal mapping using a mean apparent magnetic concentration (AMCFe3O4 ¼ [observed saturation magnetizations (Ms)/Ms of Fe3O4]) could be highly informative on the investigation of spatio-temporal pollution characteristics in urban areas (Kim et al., 2009). All of these previous works have demonstrated that magnetic investigation on road dust is best suited for an effective spatial and temporal pollution monitoring in wide urban areas. This paper reports a study of the magnetic properties and their association with heavy metals of road dusts collected spatially from four segments of the lakeshore roads encircling one of the major basin (with an area of 27.8 km2) of the East Lake in Wuhan, China, with the aim to delineate the source of pollutants, and to examine the applicability of magnetic measurements for mapping urban environments within such a small area.
Table 1 Description of sampling locations of road dusts. Location
Description
Sampling points
Donghu Hospital / Moshan Mountain (DHE)
Hills, sanatoriums and Moshan Park on the roadside (average traffic density: 13.3 vehicles min1) Both sides are lake, the closest segments to the upwind industrial areas (average traffic density: 12.3 vehicles min1) Resident area on the roadside, heavy traffic (average traffic density: 32.6 vehicles min1) Campus and government on the roadside (average traffic density: 24 vehicles min1)
DHE-01eDHE-13 (“C”, n ¼ 13)
Liyuan Square / Moshan Mountain (DHN) Zhongnan Hospital / Liyuan Square (DHW) Zhongnan Hospital / Donghu Hospital (DHS)
DHN-01eDHN-38 (“-”, n ¼ 38) DHW-01eDHW-16 (“:”, n ¼ 16) DHS-01eDHS-13 (“A”, n ¼ 30)
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Table 2 Summary of magnetic parameters of road dusts. DHE (n ¼ 13)
DHN (n ¼ 38)
DHW (n ¼ 16)
DHS (n ¼ 30)
All (n ¼ 97)
clf (108 m3 kg1)
Range Mean SD
52.59e212.93 109.32 47.16
67.24e293.53 162.60 57.39
75.86e265.09 148.52 51.23
44.40e198.28 93.23 40.01
44.40e293.53 131.68 58.28
cfd%
Range Mean SD
0.3e1.8 0.9 0.46
0.0e5.0 0.9 0.96
0.0e1.9 1.1 0.50
0.3e3.1 1.1 0.65
0.0e5.0 1.0 0.75
ARM (105 Am2 kg1)
Range Mean SD
33.60e90.83 60.93 18.99
32.47e139.61 80.92 24.65
27.51e75.57 45.37 11.84
17.79e77.88 46.20 13.36
17.79e139.61 61.64 24.92
SIRM (103 Am2 kg1)
Range Mean SD
56.48e197.28 112.91 43.51
64.74e300.21 153.59 60.06
48.05e172.83 109.34 38.99
43.50e161.51 82.92 27.73
43.50e300.21 118.98 54.75
Magnetic parameters
2. Materials and methods 2.1. Study area and samples Wuhan, located in the middle reaches of the Yangtze River, is the capital of Hubei Province and the largest city in central China. Its population is approximately 8.18 million in the end of 2006, approximately 4.3 million of them reside in nine urban core districts within an area of 201 km2. The climate of the area is humid sub-tropical with an average annual temperature of 15.8e17.5 C and annual rainfall of 1269 mm. The prevailing wind direction is towards northwest in summer and southeast in winter. The East Lake, located in the northeast of Wuhan (Fig. 1), is the largest city lake in China. A number of roadway dikes have been constructed across the lake, dividing it into several water bodies; Guozheng and Tanglin Lakes are the two major basins. The Wuhan Iron and Steel Company (WISC), the third largest iron and steel consortium in China, and the Qingshan Thermal Power Plant (QTPP) that is a coalburning plant and has a total installed capacity of 986,000 kW, are
situated northeast of the East Lake. A heavy industrial area consisting of iron and steel processing, cement production, foundries, and machinery factories and coking plants, etc., is located to the north of the lake (Fig. 1). Road dust samples were collected from four segments of the Donghu Road encircling the Guozheng Lake over a 7-day dry weather period in September 2002. Sampling locations are shown in Fig. 1 and the details are given in Table 1. At each sampling site, road dust was collected by ground sweeping with a small paint brush from a square of 1e2 m2, and transferred to clean, self-sealed polyethylene bags. Totally, ninety-seven dust samples were obtained. In the laboratory, all samples were air-dried at room temperature and passed through a 1-mm sieve to remove refuse and small stones. 2.2. Magnetic measurements Magnetic low-field susceptibilities were measured at two frequencies (0.47 kHz and 4.7 kHz) using a Bartington Instruments
Fig. 2. Box plot of low-field magnetic susceptibility (clf), frequency-dependent magnetic susceptibility (cfd%), ARM, and SIRM for road dust. The boxes in the box plot give the interquartile range of the values, the lines in the boxes the median values.
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MS2B sensor. The values are expressed as mass-specific susceptibility clf and chf, respectively. Frequency-dependent susceptibility was then calculated and expressed as a percentage cfd% ¼ (clf chf)/ clf 100%. An anhysteretic remanent magnetization (ARM) was imparted using a peak alternating field (AF) of 100 mT with a direct current (DC) bias field of 0.05 mT parallel to the AF, and was measured using a Molspin magnetometer. Isothermal remanent magnetization (IRM) measurements were carried out using an ASC Scientific model IM-10 impulse magnetizer and Molspin magnetometer. The IRM acquired in a peak field of 1 T was regarded as saturation IRM (SIRM). In addition, a number of representative
samples were magnetized in stepwise increasing DC fields to obtain IRM acquisition curves, 12 steps from 7 to 1100 mT were used. Magnetic hysteresis loops for representative subset of samples were measured using a Princeton Alternating Gradient Force Magnetometer (Model 2900 AGM) at the Institute of Rock Magnetism, University of Minnesota, USA, the maximum applied field was 1.0 T. Hysteresis parameters, saturation magnetization (Ms), saturation remanence (Mrs) and coercive force (Bc) were calculated after paramagnetic slope correction. To obtain the remanence coercivity (Bcr), after reaching 1.0 T, samples were remagnetized using backward field method.
Fig. 3. Isothermal remanent magnetization (IRM) acquisition and back-field demagnetization curves for selected road dust samples.
T. Yang et al. / Atmospheric Environment 44 (2010) 1175e1185
Temperature dependence of the magnetic low-field susceptibility was determined using a Bartington Instruments MS2 system with a high temperature attachment in air. Measurements were performed from room temperature up to 700 C, with a measurement interval of 2 C and a heating and cooling rate of 5 C min1 and 10 C min1, respectively. 2.3. Heavy metal analyses About 3 g of road dust mixed with low-pressure polyethylene powder is pressed to obtain a powder pellet with diameter of ca. 34 mm. The analyses for major elements was performed on the pellet using a Siemens SRS303 X-ray fluorescence (XRF) spectrometer equipped with a Rh-anode X-ray tube and the Siemens SPECTRA AT evaluation software at the State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences. The analytical accuracy was <5% and precision was <2%. 3. Results 3.1. Magnetic properties The magnetic parameters of road dusts are summarized in Table 2. The clf of road dusts in DHN and DHW segments are generally higher than those in DHE and DHS (Table 2 and Fig. 2). The average clf values of road dusts from DHN, DHW, DHE and DHS are 162.60, 148.52, 109.32, and 93.23 108 m3 kg1, respectively.
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The cfd% values of road dusts range from 0.0% to 5.0% with mean values of w1%. ARM and SIRM of road dusts peak at DHN. The mean value of ARM is the lowest in DHW, whilst the mean value of SIRM is the lowest in DHS (Fig. 2c, d). IRM acquisition and back-field demagnetization curves for representative samples are shown in Fig. 3. IRM acquisition curves rapidly reach saturation below 300 mT, regardless of sampling locations; whilst back-field demagnetization curves display much softer behaviour, with Bcr lower than 30 mT. All hysteresis loops are thin, closed and approach magnetic saturation in the field of 300 mT (Fig. 4), indicating the magnetic minerals are dominated by low coercivity ferrimagnetic minerals. This finding can be confirmed by the ceT curves (Fig. 5). For road dusts from DHE, DHN and DHS segments, heating runs show the presence of several magnetic phases or phase transitions (Fig. 5a): The increased susceptibility above ca. 200 C may be due to phase transition (Kontny et al., 2000); The susceptibility increased gradually up to ca. 460 C, which might represent the Hopkinson peak on heating, and subsequently decreased up to 580 C, revealing the presence of Fe3O4 as the dominant magnetic carrier. For those from DHW segment (Fig. 5b), the Tc of 580 C confirms the present of magnetite (Dunlop and Özdemir, 1997). The cooling curve shows a significant decrease in susceptibility (Fig. 5b) could be due to that some foreign atoms diffuse into the crystal lattice or the alterations of magnetite-like materials to unidentified non-ferrimagnetic materials during heating, and hence lowering the magnetization and the Tc.
Fig. 4. Magnetic hysteresis loops for representative road dust samples.
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V and Cr are the lowest. In DHN segment, mean concentrations of Fe, V, Pb, Ni, Mn, Cr and Co are the highest (Table 3). Mean concentration of Zn is the highest in segment DHE (Fig. 6c), where the mean concentrations of Pb, Mn, Cr and Co follow those in DHN. Mean concentrations of Fe and Cu in segment DHS are the lowest. The Tomlinson pollution load index (PLI) (Angulo, 1996) has been often used to assess the relative heavy metal toxicity and how much a sample exceeds the element concentrations for natural environments. The PLI index is defined as the nth root of the multiplication of the concentration factors (CFHM), where CFHM is the ratio between the concentration of each heavy metal (CHM) to its corresponding background value (Cbackground) or the lowest concentration value detected for each heavy metal (Clowest). In the present study, the lowest concentration value for each element (Clowest) in the whole set of 31 samples was used to calculate the PLI index.
CFHM ¼ CHM =Clowest ;
(1)
and
PLI ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi p n CFHM1 CFHM2 . CFHMn :
(2)
Table 3 shows that the PLI in DHN ranges from 1.23 to 2.08 with the highest mean value (up to 1.69), and followed by DHE (1.51), DHW (1.38) and DHS (1.36). 3.3. Associations of heavy metals with magnetic parameters
Fig. 5. Representative types of temperature-dependent magnetic susceptibility curves. Each curve was normalized with its corresponding magnetic susceptibility at room temperature cRT. The black and grey lines denote heating and cooling runs, respectively.
3.2. Heavy metal concentrations Heavy metal concentrations are given in Table 3 and the comparison of concentrations of Fe, Cu, Pb, Zn, Mn and Co are shown in Fig. 6. The highest mean concentration of Cu is present in DHW segment (Fig. 6b), where mean concentrations of Pb, Mn,
Table 4 lists the Pearson's correlation coefficients between element concentrations and magnetic parameters. The correlation matrix indicates that clf significantly correlates with Fe, Cu and Co, however, ARM, and SIRM have a much stronger correlation with all the heavy metals except for Pb and Zn that has a very weak negative correlation for all magnetic parameters (Table 4). The differences in the correlations between magnetic parameters and heavy metals possibly due to the dependence of magnetic concentration-related parameters on mineral phase and grain size, and/or to the intermixture of elements from different sources and their association with different magnetic minerals, and also to various physical and chemical effects that occurred during their transportation and after deposition.
Table 3 Heavy metal concentrations and PLI for road dusts. DHE (n ¼ 5)
DHN (n ¼ 11)
DHW (n ¼ 6)
DHS (n ¼ 9)
All (n ¼ 31)
Fe (%)
Range Mean SD
2.81e3.13 2.94 0.12
2.22e5.35 3.87 1.06
2.59e3.56 3.07 0.41
1.97e3.16 2.66 0.42
1.97e5.35 3.21 0.85
Zn (mg kg1)
Range Mean SD
176e260 238.2 35.0
160e299 215.9 43.5
197e260 218.5 23.4
136e355 230.3 68.0
136e355 224.2 46.8
V (mg kg1)
Range Mean SD
55.2e71.7 62.6 5.9
58.7e90.3 71.8 10.3
52.0e63.8 58.2 3.8
51.0e76.4 64.5e8.1
51.0e90.3 65.6 9.3
Pb (mg kg1)
Range Mean SD
92.6e124.0 109.2 13.3
92.7e265.0 134.3 54.3
60.3e79.7 68.0 6.7
63.0e113.0 83.1 16.0
60.3e265.0 102.6 42.5
Ni (mg kg1)
Range Mean SD
19.8e28.3 24.7 3.61
24.7e45.7 31.0 7.1
23.0e32.3 27.4 3.1
21.2e38.0 25.7 5.4
19.8e45.7 27.7 5.9
Mn (mg kg1)
Range Mean SD
585e616 604.6 11.8
515e1123 686.3 163.2
478e587 538.7 37.7
439e836 542.8 118.2
439e1123 602.9 131.6
Cr (mg kg1)
Range Mean SD
68.8e86.1 79.0 7.1
68.7e103.0 82.4 12.6
58.9e74.7 66.0 6.4
54.4e96.2 70.7 12.3
54.4e103.0 75.3 12.3
Cu (mg kg1)
Range Mean SD
52.9e70.4 61.8 7.5
40.5e125.0 64.3 24.4
61.2e87.1 72.0 9.4
37.7e75.4 53.2 13.4
37.7e125.0 62.1 17.7
Co (mg kg1)
Range Mean SD
10.7e13.2 12.2 1.1
7.7e41.4 15.5 9.2
8.6e11.6 10.1 1.2
6.9e13.3 10.1 2.5
6.9e41.4 12.4 6.0
PLI
Range Mean SD
1.31e1.63 1.51 0.13
1.23e2.08 1.69 0.28
1.29e1.52 1.38 0.08
1.09e1.73 1.36 0.24
1.09e2.08 1.51 0.26
Heavy metals
T. Yang et al. / Atmospheric Environment 44 (2010) 1175e1185
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Fig. 6. Comparison of heavy metal concentrations of road dust in four segments. The boxes in the box plot give the inter-quartile range of the values, the lines in the boxes the median values.
4. Discussion 4.1. Magnetic mineralogy and granulometry The integrated magnetic results (Figs. 3e5) reveal that magnetite is the dominant ferrimagnetic phase in the road dust. cfd% is
sensitive to the superparamagnetic (SP) component, if cfd% > 4%, the assemblage of magnetic grains contains a significant portion of SP particles; in the case of cfd% < 4%, the proportion of SP particles is low (Dearing et al., 1996). Accordingly, magnetic carriers in the road dusts (with mean cfd% values of w1.0%) are predominately coarse-grained particles and the proportion of SP particles is much
Table 4 Correlation coefficients between heavy metals concentration, PLI and magnetic parameters for road dusts (n ¼ 31).
clf cfd% ARM SIRM a b
Fe
Zn
V
Pb
Ni
Mn
Cr
Cu
Co
PLI
0.740a 0.491a 0.779a 0.824a
0.106 0.290 0.059 0.072
0.278 0.331 0.441b 0.385b
0.035 0.343 0.316 0.204
0.351 0.291 0.423b 0.407b
0.349 0.298 0.552a 0.485a
0.234 0.283 0.387b 0.340
0.486a 0.474a 0.369b 0.504a
0.366b 0.362b 0.542a 0.494a
0.450b 0.531a 0.623a 0.595a
Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed).
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low. It is further confirmed by the Day plot (Day et al., 1977) in which the ratios of Mrs/Ms and Bcr/Bc occupy the single-domain and pseudo-single-domain (SD/PSD) regions (Fig. 7). A dimensionless ratio between anhysteretic susceptibility and low-field susceptibility (cARM/clf) can indicate changes in magnetite grain size if the magnetic carrier is dominantly magnetite and significant amounts of very fine SP grains are absent; a higher slope (cARM/clf) indicates a smaller magnetic grain size, because cARM is sensitive to the stable single-domain (SSD) and fine PSD ferrimagnetic grains (King et al., 1982; Evans and Heller, 2003). In Fig. 8, samples from segment DHW show relatively lower cARM/clf slopes, suggesting the presence of coarser-grained magnetite, whilst the higher cARM/clf slope for segments DHN and DHE indicate a relative smaller magnetite grain size. 4.2. Anthropogenic magnetic particles and heavy metals in the road dust Road dust is a mixture of natural and anthropogenic components, both contain magnetic mineral fractions with specific magnetic properties. Together the high values of clf and SIRM, low cfd%, and the presence of coarse-grained magnetite as the primary magnetic carrier, it reveals that magnetic properties of the road dusts are dominated by ferrimagnetic minerals with anthropogenic origins (Fialova et al., 2006; Magiera et al., 2006). In order to check the dependence of magnetic parameter and element concentrations on the traffic density or the distance to the industrial region, the values of them for 11 samples with traffic record were normalized in the following way, to obtain dimensionless values:
xnorm ¼
x xmin xmax xmin
(3)
A complicated behaviour is seen when plotting clf, ARM and SIRM against traffic density (Fig. 9a). Low values are found in places
Fig. 7. Day plot of the ratios Mrs/Ms and Bcr/Bc of road dust. Single-domain (SD), pseudo-single-domain (PSD) and multi-domain (MD) boundaries are after Dunlop (2002).
Fig. 8. Bivariate plot of anhysteretic susceptibility (cARM) versus low-field susceptibility (clf) of road dust (King et al., 1982). Numbers in parentheses in the legend are the average traffic densities in corresponding segment. Samples from the low traffic density segments DHE and DHN contain finer grains than the heavy traffic sites DHS and DHW.
with intermediate traffic densities. All three magnetic parameters behave similar. The grain size parameter ARM/clf shows a significant (at 1%) anticorrelation with increasing traffic (Fig. 9b). The ARM/clf ratio is low at high traffic density. An increased number of vehicles rises thus the contribution of larger grains to susceptibility and the ratio ARM/clf falls. Vehicle traffic emissions can hence be associated with larger grain sizes (see also Fig. 8). Most of the element concentrations do not show simple linear trends. This may be due to the presence of a second major pollution source, i.e. the industrial region where the WISC and QTPP are situated. However, iron, nickel and copper concentrations are generally higher at higher traffic densities. The significant positive correlation was found between Cu and traffic density (Fig. 9c). The above mentioned ARM/clf is hence an indirect indicator for Cu, Fe and Ni emissions resulting from vehicle traffic being related to coarser magnetic minerals. The significant correlation (at 5%) between traffic density and distance to the industrial region (Fig. 10a) indicates that the places close to the industrial region have relative low traffic density, and vice versa. Consequently, the contribution of magnetic particles and elements from the industrial emissions should be generally decreased, and that from vehicle traffic should be increased with the increasing distance to the industrial region. The intermixture of magnetic particles from these sources could hence be the reason for the double peaks in clf, SIRM and ARM at low and high traffic densities, respectively (Fig. 9a). However, for clf and SIRM, magnetic particles from both, vehicle traffic and industrial emissions contribute to the corresponding bulk values. This is not the case for the ARM which is more selective because MD and PSD grains have smaller contributions to it. Thus one sees mainly finer particle (e.g., SD grains) contributions which are apparently related to longdistance transported fly ashes and/or emissions originated from WISC and QTPP. This can be supported by the significant negative correlation (at 5%) between ARM/clf and the distance to the industrial region (Fig. 10b). If copper concentrations are mainly associated with traffic, they should be independent from the distance to the industrial region.
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Fig. 9. (a) Normalized values (cf. equation (3)) of clf, ARM and SIRM in function of number of vehicles. (b) A significant negative correlation is found between the magnetic grain size parameter ARM/clf and traffic density, while copper concentrations show a significant positive correlation with the amount of traffic (c).
In order to test this hypothesis, Cu was plotted against the distance to the industrial region. Indeed, no significant correlation with was found (Fig. 10c). In contrast, the lead concentration shows a significant (at 5%) anticorrelation with increasing distance to the industrial region (Fig. 10d), indicating that this element being present in the road dust mainly originates from industrial emissions. All other elements have no significant correlation with the distance to the industrial region. It may be caused by the intermixture of elements from different sources, and proposes that the element concentrations are not conclusive about the pollution source. 4.3. Environmental implications of magnetic characteristics of road dust To sum up, it is found that road dusts from four segments have diverse magnetic characteristics, for example, the different slopes of cARM/clf (Fig. 8), and different thermomagnetic alterations (Fig. 5). It seems that these diversities result from the different composition, grain size and concentrations of the magnetic carrier in road dusts; however, they are essentially indicative of the different sources of magnetic particles in the road dusts as discussed above. Most magnetic particles derived from vehicle emissions have been identified as a non-spherical aggregate of iron-oxides (mainly magnetite) and FeeCeS materials (Matzka and Maher, 1999; Abdul-Razzaq and Gautam, 2001; Maher et al., 2008; Lu et al., 2008), whilst, for those produced from the abrasion or corrosion of
the vehicle engine and body work, they were found in the form of an aggregate of pure Fe and AleCaeFeeKeMgeSi materials (Kim et al., 2007). However, most of the magnetic fraction (mainly magnetite/maghemite) in coal-fired fly ash is present in spherules with diameters of 2e50 mm (Strzyszcz et al., 1996; Sokol et al., 2000). Furthermore, vehicle emissions are dominated by ferrimagnetic particles whereas the fly ashes are magnetically harder due to the presence of hematite (Lecoanet et al., 2003). These differences in magnetic minerals, grain size and morphology should be responsible for the diverse magnetic properties of the road dusts from these four segments (Figs. 5 and 8), and the different relations between magnetic parameters and heavy metal concentrations with the traffic density and distances to the industrial region (Figs. 9 and 10). These results make it possible to discriminate the sources from industrial plants and road traffic using magnetic methods (Hoffmann et al., 1999; Shu et al., 2001; Lecoanet et al., 2003; Spassov et al., 2004), although the exact mechanism that led to these magnetic differences has not been fully understood. Almost all the heavy metals (except Pb and Zn) show good correlation with the magnetic remanence parameters ARM and SIRM (Table 4). These associations could be an indicative of an anthropogenic nature of the magnetic carrier, revealing that magnetic particles and heavy metals coexist in the road dusts. Among them, Fe, Mn, and Co show much strong correlation with ARM, followed by V, Ni, Cr, and Cu, suggesting that ARM is a more efficient indicator for dust heavy metals. It is also interesting to note that the Tomlinson PLI significantly correlates with magnetic
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Fig. 10. (a) Test for correlation between traffic density and distance to the industrial region for the different sampling locations, significant correlation is seen at a 5% confidence level. ARM/clf ratio (b) shows a significant correlation with the distance to the industrial region, but not for the element Copper (c). In contrast, the Lead (d) shows a significant negative correlation with the distance to the industrial area.
concentration-related parameters clf, ARM, and SIRM (Table 4), indicating that magnetic concentration is more proportional to the concentration of collective heavy metals rather than each individual concentration, and that the comprehensive evaluation of heavy metals is more reasonable. Regardless of the nature for coexistence of the heavy metals and magnetic particles in road dusts, these significant correlations make it is possible to employ magnetic measurements as a tool for mapping dust pollutions, at least in the studied area.
not conclusive about the pollution source. Significant correlations between the concentrations of the most heavy metals (e.g., Cu, Co, Cr, Fe, Mn, Ni and V) and magnetic remanence parameter ARM, pointed out the potential of magnetic parameters for simple, rapid and non-destructive proxy indications of heavy metals, although the nature of the linkage between heavy metals and magnetic components is still an open problem. It also seems to be possible to identify and distinguish different pollution sources by magnetic measurements, together with the auxiliary geochemical studies.
5. Conclusions
Acknowledgements
Magnetic properties and their associations with heavy metal contents of the road dust in East Lake area in Wuhan, China were presented. The dominant magnetic carrier of road dust is coarsegrained magnetite. Correlations between magnetic parameters and element concentrations with traffic loads and distances to the industrial region revealed that coarser magnetic grains (e.g., PSD/ MD) and elements Fe, Cu, and Ni are mainly related to vehicle traffic, while finer grains (e.g., SD) and element Pb mainly originate from long-distance transported fly ashes/emissions released by the nearby industrial plants. ARM/clf can be used as an indirect indicator for Cu, Fe and Ni emissions resulting from vehicle traffic which related to coarser magnetic minerals. Due to the intermixture of elements from different sources, their concentrations are
This study was supported by the National Natural Science Foundation of China (No. 40474025). We express our deep gratitude to Dr. Simo Spassov and an anonymous referee whose comments and suggestions greatly improved the manuscript. The authors thank Dr. Qingsong Liu for help with hysteresis loop measurements. References Abdul-Razzaq, W., Gautam, M., 2001. Discovery of magnetite in the exhausted material from a diesel engine. Applied Physics Letters 78, 2018e2019. Angulo, E., 1996. The Tomlinson pollution load index applied to heavy metal “Mussel-Watch” data: a useful index to assess coastal pollution. Science of the Total Environment 187, 19e56.
T. Yang et al. / Atmospheric Environment 44 (2010) 1175e1185 Davila, A.F., Rey, D., Mohamed, K., Rubio, B., Guerra, A.P., 2006. Mapping the sources of urban dust in a coastal environment by measuring magnetic parameters of Platanus hispanica leaves. Environmental Science and Technology 40, 3922e3928. Day, R., Fuller, M., Schmidt, V.A., 1977. Hysteresis properties of titanomagnetites: grain-size and compositional dependence. Physics of the Earth and Planetary Interiors 13, 260e267. Dearing, J.A., Hay, K.L., Baban, S.M.J., Huddleston, A.S., Wellington, E.M.H., Loveland, P.J., 1996. Magnetic susceptibility of soil: an evaluation of conflicting theories using a national data set. Geophysical Journal International 127, 728e734. Dunlop, D.J., 2002. Theory and application of the Day plot (Mrs/Ms versus Hcr/Hc) 1. Theoretical curves and tests using titanomagnetite data. Journal of Geophysical Research 107 (B3), 2056. doi:10.1029/2001JB000486. Dunlop, D.J., Özdemir, Ö, 1997. Rock Magnetism: Fundamentals and Frontiers. Cambridge University Press, Cambridge, p. 573. Evans, M.E., Heller, F., 2003. Environmental Magnetism: Principles and Applications of Enviromagnetics. Academic Press, London, p. 299. Fialova, H., Maier, G., Petrovský, E., Kapi cka, A., Boyko, T., Scholger, R., MAGPROX Team, 2006. Magnetic properties of soils from sites with different geological and environmental settings. Journal of Applied Geophysics 59, 273e283. Gautam, P., Blaha, U., Appel, E., 2005. Magnetic susceptibility of dust-loaded leaves as a proxy of traffic-related heavy metal pollution in Kathmandu city, Nepal. Atmospheric Environment 39, 2201e2211. Goddu, S.R., Appel, E., Jordanova, D., Wehland, F., 2004. Magnetic properties of road dust from Visakhapatnam (India)erelationship to industrial pollution and road traffic. Physics and Chemistry of the Earth 29, 985e995. Hanesch, M., Scholger, R., Rey, D., 2003. Mapping dust distribution around an industrial site by measuring magnetic parameters of tree leaves. Atmospheric Environment 37, 5125e5133. Harrison, R.M., Yin, J., 2000. Particulate matter in the atmosphere: which particle properties are important for its effects on health? Science of the Total Environment 249, 85e101. Hoffmann, V., Knab, M., Apple, E., 1999. Magnetic susceptibility napping of roadside pollution. Journal of Geochemical Exploration 99, 313e326. Jordanova, D., Jordanova, N., Hoffmann, V., 2006. Magnetic mineralogy and grain-size dependence of hysteresis parameters of single spherules from industrial waste products. Physics of the Earth and Planetary Interiors 154, 255e265. Kim, W., Doh, S.J., Park, Y.H., Yun, S.T., 2007. Two-year magnetic monitoring in conjunction with geochemical and electron microscopic data of roadside dust in Seoul, Korea. Atmospheric Environment 41, 7627e7641. Kim, W., Doh, S.J., Yu, Y., 2009. Anthropogenic contribution of magnetic particulates in urban roadside dust. Atmospheric Environment 43, 3137e3144. Kim, W., Doh, S.J., Yu, Y., Lee, M., 2008. Role of Chinese wind-blown dust in enhancing environmental pollution in Metropolitan Seoul. Environmental Pollution 153, 333e341. King, J.W., Banerjee, S.K., Marvin, J., Özdemir, Ö, 1982. A comparison of different magnetic methods of determining the relative grain size of magnetite in natural materials: some results from lake sediments. Earth and Planetary Science Letters 59, 404e419.
1185
Kontny, A., de Wall, H., Sharp, T.G., Pósfai, M., 2000. Mineralogy and magnetic behavior of pyrrhotite from a 260 C section at the KTB drilling site, Germany. American Mineralogist 85, 1416e1427. Lecoanet, H., Léveque, F., Ambrosi, J.P., 2003. Combination of magnetic parameters: an efficient way to discriminate soil-contamination sources (south France). Environmental Pollution 122, 229e234. Lu, S.G., Zheng, Y.W., Bai, S.Q., 2008. A HRTEM/EDX approach to identification of the source of dust particles on urban tree leaves. Atmospheric Environment 42, 6431e6441. Maher, B.A., Moore, C., Matzka, J., 2008. Spatial variation in vehicle-derived metal pollution identified by magnetic and elemental analysis of roadside tree leaves. Atmospheric Environment 42, 364e373. Magiera, T., Strzyszcz, Z., Kapi cka, A., Petrovský, E., MAGPROX Team, 2006. Discrimination of lithogenic and anthropogenic influences on topsoil magnetic susceptibility in Central Europe. Geoderma 130, 299e311. Matzka, J., Maher, B.A., 1999. Magnetic biomonitoring of roadside tree leaves: identification of spatial and temporal variations in vehicle-derived particulates. Atmospheric Environment 33, 4565e4569. Muxworthy, A.R., Matzka, J., Petersen, N., 2001. Comparison of magnetic parameters of urban atmospheric particulate matter with pollution and meteorological data. Atmospheric Environment 35, 4379e4386. Querol, X., Alastuey, A.S., Lopez-Soler, A., Mantillai, E., Plana, F., 1996. Mineral composition of atmospheric particulates around a large coal-fired power station. Atmospheric Environment 30, 3557e3572. Shilton, V.F., Booth, C.A., Smith, J.P., Giess, P., Mitchell, D.J., Williams, C.D., 2005. Magnetic properties of urban street dust and their relationship with organic matter content in the West Midlands, UK. Atmospheric Environment 39, 3651e3659. Shu, J., Dearing, J.A., Morse, A.P., Yu, L., Yuan, N., 2001. Determining the sources of atmospheric particles in Shanghai, China, from magnetic and geochemical properties. Atmospheric Environment 35, 2615e2625. Sokol, E.V., Maksimova, N.V., Volkova, N., Nigmatulina, E., Frenkel, A., 2000. Hollow silicate microspheres from fly ashes of the Chelyabinsk brown coals (South Urals, Russia). Fuel Processing Technology 67, 35e52. Spassov, S., Egli, R., Heller, F., Nourgaliev, D.K., Hannam, J., 2004. Magnetic quantification of urban pollution sources in atmospheric particulate matter. Geophysical Journal International 159, 555e564. Strzyszcz, Z., Magiera, T., Heller, F., 1996. The influence of industrial emissions on the magnetic susceptibility of soils in Upper Silesia. Studia Geophysica et Geodetica 40, 276e286. Xia, D.S., Chen, F.H., Bloemendal, J., Liu, X.M., Yu, Y., Yang, L.P., 2008. Magnetic properties of urban dustfall in Lanzhou, China, and its environmental implications. Atmospheric Environment 42, 2198e2207. Xie, S., Dearing, J.A., Bloemendal, J., Boyle, J.F., 1999. Association between the organic matter content and magnetic properties in street dust, Liverpool, UK. Science of the Total Environment 241, 205e214. Yang, T., Liu, Q.S., Chan, L.S., Cao, G.D., 2007. Magnetic investigation of heavy metals contamination in urban topsoils around the East Lake, Wuhan city, China. Geophysical Journal International 171, 603e612. Yang, T., Liu, Q.S., Zeng, Q.L., Chan, L.S., 2009. Environmental magnetic responses of urbanization processes: evidence from lake sediments in East Lake, Wuhan, China. Geophysical Journal International 179, 873e886.