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Atmospheric Environment 46 (2012) 289e298

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Atmospheric Environment

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Magnetic properties and correlation with heavy metals in urban streetdust: A case study from the city of Lanzhou, China

Guan Wang a,b,*, Frank Oldfield c, Dunsheng Xia a, Fahu Chen a, Xiuming Liu a,d, Weiguo Zhang b

aKey Laboratory of the West Environmental System, Lanzhou University, Lanzhou 730000, Chinab State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, Chinac School of Environmental Sciences, University of Liverpool, Liverpool, UKdDepartment of Physical Geography, Macquarie University, NSW 2109, Australia

a r t i c l e i n f o

Article history:Received 29 March 2011Received in revised form26 September 2011Accepted 27 September 2011

Keywords:Magnetic propertiesHeavy metalStreet dustLanzhou city

* Corresponding author. State Key Laboratory of EstEast China Normal University, Shanghai 200062, PR Cfax: þ86 21 62546441.

E-mail address: wangguan.lzu@hotmail.com (G. W

1352-2310/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.atmosenv.2011.09.059

a b s t r a c t

We report results obtained from magnetic and geochemical measurements of 71 street dust samplesfrom four distinct districts (residential, commercial and industrial) in Lanzhou, an industrial city in China.Magnetic properties and the concentrations of 17 elements (As, Ba, Bi, Cr, Cu, Mn, Ni, Pb, Ti, Zn, Fe, Si, Na,Mg, K, Ca, Al) are reported for each sample. Ferrimagnetic mineral concentrations are generally high inLanzhou street dust, mainly due to grains above Stable Single Domain size. The highest concentrations ofmagnetic materials and heavy metals are in dusts from the two main industrial districts, Xigu andChengguan. In the least polluted Anning district, some samples have magnetic properties more probablyderived from arid areas to the north of the city. The two main industrial areas also show some differencesin magnetic mineral assemblages suggesting that magnetic properties and magnetic-metal correlationsmay be of use in ascribing heavy metal contamination to distinct sources. Geochemical studies and thesignificant positive correlation between magnetic concentrations and those for Fe, As, Cu, Mn, Ni, Pb andZn confirm that much of the heavy metal contamination in the study area is linked to combustionderived particulate emissions. The results confirm that a combined magnetic and geochemical approachcan provide useful information on the types, levels and sources of heavy metals in street dust.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

In urban environments, many kinds of anthropogenic activitygive rise to harmful emissions. Heavy metal pollution is oftenrelatively more severe in cities than other types of pollution.Moreover, heavy metal elements in soils, sediments and surfacedusts are often not decomposed by microorganisms, and maybecome enriched through the food chain. The consequences can beharmful to human health. In the past decades, many studies havebeen carried out on heavy metal pollution in urban areas andseveral shows that heavy metal concentrations may reach highconcentrations in soils and street dusts (Gautam et al., 2005; Luet al., 2007).

Since the early work of Scoullos et al. (Scoullos et al., 1979),magnetic measurements have been widely used in studies ofparticulate pollution in urban areas (Georgeaud et al., 1997;

uarine and Coastal Research,hina. Tel.: þ86 21 62233461;

ang).

All rights reserved.

Charlesworth and Lees, 2001). However, relatively few studies haveshed light on the relationship between magnetic minerals andheavy metals in street dusts. Street dust often becomes a sink forboth industrial and vehicle-generated pollutants, including wasteincineration residues, vehicle exhaust emissions, and products oftyre wear, metallic fragments, fossil fuel combustion emissions andgarden soil. In urban areas, both toxic heavy metals (such as lead)and magnetic minerals (magnetite and hematite) typicallycontribute to street dust (Xie et al., 2001); moreover, several studiessuggest that heavy metal elements in urban sediments may havedistinctive magnetic signatures (Shu et al., 2000; Xie et al., 2001;Robertson et al., 2003; Gautam et al., 2005).

In the present study, we focus on street dusts in view of therelative ease with which they may be re-suspended and of thepotential health consequences of their inhalation or ingestion. Ourmain aims are as follows;

1. Plot and analyze the spatial variations in heavy metal concen-trations in street dusts within the city of Lanzhou.

2. Summarize the magnetic characteristics of each dust sampleusing the results of a previously published rock-magnetic study(Wang et al., 2008).

G. Wang et al. / Atmospheric Environment 46 (2012) 289e298290

3. Establish the degree to which each of the elements recorded islinked to magnetic mineral concentrations on both a city-wideand a district-by-district basis.

4. Establish the extent to which the elements analyzed are asso-ciated with combustion related particulate discharges.

5. Link the main pollutants to particular industrial zones andpossible sources.

2. Materials and methods

Lanzhou City, situated on the upper course of the Yellow River,in Gansu Province, is a heavy industrial city. It is located close to theboundaries between the Tibetan Plateau, Inner Mongolia Plateauand Loess Plateau and experiences a semi-arid climate (Fig. 1). Thecity hosts highly developed chemical and petroleum industries,non-ferrous metal works, heavy equipment manufacture, and coal-fired electrical power generation plants, and suffers from large duststorms kicked up from the Gobi Desert, especially in the winter andspring. The main particulate sources are therefore airborne dust,industrial discharges, and coal combustion and traffic emission.

Xigu, the most westerly district (Fig. 1) is the main industrialregion of Lanzhou with major petro-chemical installations, powerplants and other heavy industries, including aluminum and textiles.Chengguan, in the eastern part of the city is a large residential areawith a coal-fired power station and a wide variety of agricultural,engineering and pharmaceutical industries, research institutes andthe main railway station. Within the Qilihe district, to the south-east of Xigu, manufacturing industries including petro-chemicals,machinery, textiles, electrical appliances, locomotives, plasticsand food processing are located. Anning, lying to the north of Xiguand Qilihe on the opposite bank of the Yellow River is largelyresidential with agriculture and education the main activities.

The city is located on a bend in the narrow river valley as a result ofwhich it is hemmed in with no free air flow. Therefore, the dischargeof industrial and domestic pollutants, the long, narrow, river valleylocation, and presence of un-vegetated surfaces nearby all conspire toensure ideal conditions for high levels of dust deposition.

A total of 71 street dust samples were collected by brush (Kimet al., 2009) in January 2006 from pedestrian streets, gardens,roadways with various traffic densities, factories and hospitals infour districts of Lanzhou: Xigu, Anning, Qilihe and Chengguan(Fig. l). Samples were first put in pocket-sized sealable plastic bagsand air-dried in the laboratory, and then a 1-mm sieve was used toremove coarse debris and small stones before measurement in thelaboratory.

Fig. 1. Sketch map depicting street d

2.1. Magnetic measurements

Initial, low-field, mass-specific, magnetic susceptibility (c)was measured on 4 g samples using a dual-frequency (470 and4700 Hz) Bartington Instruments MS2 sensor at the 0.1 scale.Both low- and high-frequency susceptibility were measured (cLFand cHF), allowing the frequency-dependent susceptibility to becalculated (cfd%) (cfd% ¼ [cLF � cHF]/cLF � 100). AnhystereticRemanent Magnetization (ARM) was induced in a DC biasing fieldof 0.04 mT imposed on a peak AF field of 100 mT using a MolspinAF demagnetizing unit. Measurements are expressed as suscep-tibility of ARM (cARM 10�8 m3 kg�1) by dividing the remanence bythe DC bias field in Ampères. Isothermal Remanent Magnetiza-tion (IRM) was induced in a series of successively higher fields upto a maximum ‘saturation’ field of 1000 mT (SIRM) followed bya series of increasing fields in the reverse direction (backfields),using a Molspin pulse magnetizer. All IRMs were measured ina Molspin slow-speed spinner magnetometer. Magnetic param-eters are expressed on both mass-specific and quotient bases inorder to give quantitative and qualitative information. Fromthese data, a number of standard parameters and ratios werecalculated, as shown in Table 1.

Low-frequency magnetic susceptibility cLF indicates the totalcontribution of magnetic minerals in the sample and in all exceptthe most weakly magnetic samples, the dominant contribution isfrom ferrimagnetic minerals (magnetite and maghemite). SIRMprovides a rough measure of the concentration of all the rema-nence carrying magnetic minerals present, but it under-representsthe anti-ferromagnetic contributions (here dominantly hematite)by around two orders of magnitude (Peters and Dekkers, 2003).Here, HIRM (magnetically ‘Hard’ IRM) may be regarded as roughlyproportional to the changing concentrations of hematite, but withthe reservations set out in Liu et al. (Liu et al., 2007). SOFT IRMapproximately reflects remanence bearing ferrimagnetic compo-nent, particularly the contribution of magnetic grains in themultidomain (MD) range and at the superparamagnetic/stablesingle domain (SP/SD) border, with low coercive force. cARM issensitive to the content of stable single domain (SD) ferrimagneticgrains (Maher, 1988). cfd% reflects the relative importance offerrimagnetic grains close to the SP/SD border and, like thequotient cARM/SIRM, may be used as an indicator of the relativeimportance of fine grains. ‘S�300’, as presented here, and with thereservations noted by Liu et al. (Liu et al., 2007), give an indicationof changes in the relative contribution to SIRM from the hardremanence components. Samples with a high S�300 reflect

ust sampling sites in Lanzhou.

Table 1Magnetic parameter values of street dust samples taken in Lanzhou.

Measurement Units Range Mean

cLF 10�8 m3 kg�1 111.42e987.90 449.88cfd% % 1.23e6.25 2.22SIRM 10�5Am2 kg�1 1654.38e14256.71 6618.41HIRM 10�5Am2 kg�1 67.78e275.59 150.81SOFT 10�5Am2 kg� 500.28e4779.45 2117.11cARM 10�8 m3 kg�1 387.46e1689.01 956.49cARM/cLF N/A 1.15e4.56 2.27cARM/SIRM 10�3m A�1 0.10e0.30 0.15S�300 % 92e97 95

G. Wang et al. / Atmospheric Environment 46 (2012) 289e298 291

assemblages in which the magnetic properties are dominated bymagnetite/maghemite; lower values indicate higher contributionsfrom anti-ferromagnetic minerals (Oldfield, 1991; Verosub andRoberts, 1995). Further information on the interpretation of themagnetic properties shown may be found in the references citedand in Walden et al. (Walden et al., 1999).

2.2. Geochemical analysis

For geochemical analysis, in accordance with the require-ments of the XRF instrument all the previously sieved subsam-ples were finely ground, to pass through a 200 (75 mm) meshsieve and pressed into a tablet. As, Ba, Bi, Cr, Cu, Mn, Ni, Pb, Ti, Zn,

Fig. 2. Variations in magnetic properties

Fe, K, Ca, Na, Mg, Al, Si concentrations were determined usinga PANalytical PW2403/00 X-ray fluorescence (XRF) analyzer.Blank samples and the China national reference material GSD-9(Wei, 1990) were used for accuracy control and the analyticalprecision is better than 10%. The repeatability of the measure-ments was confirmed by analyzing separate aliquots of 25% of thetotal sample set. XRF has been widely used in street dust samplesanalysis (Xie et al., 2001; Yeung et al., 2003; Li et al., 2010; Yanget al., 2010).

2.3. Stastistical analysis

Statistical analyses of the magnetic and geochemical datawere carried out using SPSS for Windows 15.0 software. Corre-lation coefficients and the associated levels of significance (p)were used to establish the relationship between heavy metallevels and magnetic parameters in the street dusts. PrincipalComponent Analysis (PCA) was adopted for data treatment inorder to aid the classification and characterization of the samplesand to provide preliminary indications of associations andsources. The components with eigenvalue >1 are extracted inthis study.

3. Results

For the purposes of the present paper, the results are discussedin terms of the four districts spanned by the transect.

for the Lanzhou street dust samples.

G. Wang et al. / Atmospheric Environment 46 (2012) 289e298292

3.1. Magnetic measurements (Fig. 2)

All the indicators of magnetic concentrations (cLF, SIRM, ‘Soft’IRM, HIRM and cARM) are strongly correlated throughout the wholeset of measurements; r-values range from 0.675 to 0.991 (meanr ¼ 0.8726) with all correlations significant at the 0.01 level(2-tailed). Maximum concentrations occur in the Xigu and, in thecase of the remanence measurements, the Chengguan district, ateach end of the transect. The Anning district has consistenly lowvalues, but the lowest individual sample values come from theeastern border of the Qilihe district. S�300 values vary within rathernarrow ranges, from 96% to99%. cfd% and cARM/SIRM are closelyparallel and provide mutually independent indications of ferri-magnetic grain size. Peaks in the Anning and Qilihe district samplesmostly correspond with low values for magnetic concentrations.cARM/cLF values mostly parallel those for cfd% and cARM/SIRM.

Fig. 3. Variations in element concentration

3.2. Element concentrations (Fig. 3)

Only three elements, Bi, Cr and Ni show peak concentrationsexclusively in the westerly, Xigu district and, in the case of Cr,minimum values in Chengguan. Fe and Mn have maximum valuesin Xigu and Chengguan, though isolated samples betweenalsoshow peaks. As and Ba show little systematic variation in Xigudistricts, though both show strong variability within the samplesfrom Anning and Chengguan. Cu shows rather consistent valuesexcept for a strong peak in the Qilihe set. Zn shows strongbetween-sample variability especially in the central and westernparts of the transect. Pb, show peak values in Chengguanand strong variability in all but the Xigu and Anning sample set.

Of the major elements also measured (K, Ca, Na, Mg, Al and Si),only Al shows peak concentrations in the Xigu district. K, Ca andMgrather consistent values in all four districts, though isolated

s for the Lanzhou street dust samples.

Table 2The variations of cLF and heavy metal concentration of street dust in different cities.

City Name cLF (10�8m3 kg�1) Pb (mg kg�1) Zn (mg kg�1) Cu (mg kg�1) Cr (mg kg�1) Mn (mg kg�1) Fe (%)

Lanzhou (this) 442.42 62.65 296.92 72.97 62.14 592.41 3.39Xi’an (Li et al., 2010) 487.05 / / / / / /Shanghai (Tanner et al., 2008; Jia, 2010) 530.11 148 699 141 242 904 4.65Beijing (Tanner et al., 2008;

Zheng and Zhang, 2008)554.40 61 214 42 86 552 2.79

Wuhan (Yang et al., 2010) 131.68 / / / / / /Hongkong (Tanner et al., 2008) / 240 4024 534 324 639 3.98Seoul (Kim et al., 2007) 62.00 144 795 396 151 439 1.88Liverpool (Xie et al., 2001) 502.80 892 1469 / / / 4.42

G. Wang et al. / Atmospheric Environment 46 (2012) 289e298 293

samples also show peaks. Si and Na especially show strongbetween-sample variability. Table 2 compares cLF and elementconcentrations reported here in Lanzhou with those reported forseveral other major cities for which comparable data have beenobtained.

3.3. General correlations between element concentrations andselected magnetic properties

Table 3 shows the correlations for the sample set as a whole. Al,Bi, Cr and Ca show no significant positive correlations withmagnetic concentrations. All the heavy metals and other elementsof predominantly anthropogenic origin (As) show positive corre-lations with all themagnetic concentration indicators and in almostall cases, the correlations are significant at the 0.01 level. Both cfd%and cARM/SIRM consistently show mainly weak, negative correla-tions with all elements except Ti and most of major elements (e.g.Si, Mg and K). The latter showmainly negative correlations with themagnetic concentration indicators.

3.4. District-based correlations between heavy metalconcentrations and selected magnetic properties

Fe is correlated with magnetic concentrations in all but theAnning District. For all other elements, there are clear between-district variations in the element-magnetic concentration correla-tions. As and Ba show the strongest correlations in the Qilihedistrict though Ba shows some degree of correlation in all districts.Cr shows a positive correlation with HIRM only in the Xigu set. Cushows no significant correlations in the Xigu set, and elsewhere, thecorrelations with the ferrimagnetic indicators are generally more

Table 3Pearson Correlation coefficients for selected magnetic properties and heavy metal conce

cLF cfd% SIRM H

As 0.339 (**) �0.20 0.461 (**)Ba 0.587 (**) �0.22 0.712 (**)Bi 0.12 �0.11 0.09Cr 0.04 �0.21 �0.02 �Cu 0.464 (**) �0.308 (**) 0.525 (**)Mn 0.546 (**) 0.00 0.625 (**)Ni 0.371 (**) �0.275 (*) 0.382 (**)Pb 0.458 (**) �0.327 (**) 0.519 (**)Ti 0.23 0.03 0.372 (**)Zn 0.481 (**) �0.357 (**) 0.474 (**)Fe 0.770 (**) �0.23 0.830 (**)Si �0.380 (**) 0.08 �0.507 (**) �Mg �0.500 (**) 0.515 (**) �0.465 (**) �K �0.705 (**) 0.523 (**) �0.753 (**) �Ca �0.17 0.17 �0.16 �Na 0.274 (*) �0.13 0.322 (**)Al 0.11 �0.02 0.13

**. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed).N ¼ 71.

significant than those with HIRM. In the case of Mn, only the Qiliheset fails to show positive correlations with magnetic concentra-tions. Ni shows the strongest correlations in the Chengguansamples whereas Pb shows the strongest correlations in Qilihe andXigu. Ti is significantly correlated with HIRM in the Qilihe andChengguan sets andmore generally in the Xigu set. The only districtwith strong positive correlations for Zn is Qilihe. Over 60% of thecorrelations with cfd% are negative.

We summarize below the main elementemagnetic concentra-tion correlations for each district as set out in Table 4.

(i). Xigu: Pb and Bi (mainly with ferrimagnetic indicators); Fe andto a lesser extent, Cr, Ni and Ti, each of which aremore stronglycorrelated with the anti-ferromagnetic indicator, HIRM.

(ii). Anning: Ba, Cu and Mn.

(iii). Qilihe: all elements except for Cr and Mn. Cu correlates withthe ferrimagnetic indicators; Ti with HIRM.

(iv). Chengguan: all elements except Zn. In the case of Ti and Zn, thesignificant correlations are with HIRM only.

From the above summary, it is clear that the general correlationssummarized in Table 3 hide considerable regional diversity withregard to element-magnetic linkages. These are considered morefully in the Discussion.

3.5. PCA analysis

The results of the PCA for selected magnetic properties andelement concentrations are shown in Table 5b, five principal

ntrations for all the street dust samples in Lanzhou.

IRM SOFT cARM/SIRM S�300

0.476 (**) 0.405 (**) �0.253 (*) 0.190.622 (**) 0.691 (**) �0.247 (*) 0.295 (*)0.06 0.09 �0.13 0.080.19 �0.03 �0.241 (*) 0.200.404 (**) 0.497 (**) �0.344 (**) 0.414 (**)0.600 (**) 0.618 (**) �0.06 0.140.304 (**) 0.350 (**) �0.330 (**) 0.235 (*)0.523 (**) 0.475 (**) �0.369 (**) 0.264 (*)0.402 (**) 0.315 (**) �0.04 0.060.397 (**) 0.446 (**) �0.384 (**) 0.358 (**)0.753 (**) 0.804 (**) �0.316 (**) 0.332 (**)0.533 (**) �0.466 (**) 0.09 �0.170.19 �0.458 (**) 0.516 (**) �0.570 (**)0.574 (**) �0.726 (**) 0.521 (**) �0.561 (**)0.06 �0.15 0.20 �0.160.295 (*) 0.318 (**) �0.11 0.200.20 0.09 �0.14 �0.07

Table 4Pearson Correlation coefficients for selected magnetic properties and heavy metal concentrations for street dust samples from each of the four district of Lanzhou.

a Xigu District b Anning District c Qilihe d Chengguan

cLF cfd% SIRM HIRM SOFT cLF cfd% SIRM HIRM SOFT cLF cfd% SIRM HIRM SOFT cLF cfd% SIRM HIRM SOFT

As 0.08 �0.27 0.14 �0.01 0.16 0.22 �0.02 0.30 0.20 0.28 0.828(**)

�0.600(**)

0.785(**)

0.631(**)

0.782(**)

0.26 0.21 0.433(*)

0.640(**)

0.35

Ba 0.47 �0.565(*)

0.574(*)

0.45 0.55 0.829(**)

�0.44 0.818(**)

0.773(**)

0.832(**)

0.776(**)

�0.499(*)

0.843(**)

0.618(**)

0.829(**)

0.537(**)

0.05 0.701(**)

0.587(**)

0.665(**)

Bi 0.585

(*)

�0.53 0.675(*)

0.54 0.642(*)

�0.16 �0.34 �0.11 �0.13 �0.10 �0.06 0.11 0.08 �0.21 0.02 �0.20 0.15 �0.19 0.07 �0.21

Cr 0.16 �0.10 0.36 0.619(*)

0.24 �0.29 0.01 �0.16 �0.47 �0.19 0.43 �0.19 0.41 0.26 0.40 �0.402(*)

0.12 �0.429(*)

�0.18 �0.439(*)

Cu 0.31 �0.07 0.22 0.10 0.25 0.686(*)

�0.15 0.712(*)

0.42 0.699(*)

0.613(**)

�0.567(*)

0.582(**)

0.19 0.597(**)

0.590(**)

�0.01 0.658(**)

0.591(**)

0.612(**)

Mn 0.668(*)

�0.645(*)

0.53 �0.04 0.663(*)

0.722(*)

�0.27 0.805(**)

0.62 0.798(**)

0.23 0.05 0.19 0.42 0.16 0.657(**)

�0.01 0.751(**)

0.736(**)

0.709(**)

Ni 0.31 �0.15 0.563(*)

0.800(**)

0.42 0.44 0.22 0.41 0.39 0.40 0.744(**)

�0.465(*)

0.754(**)

0.662(**)

0.741(**)

0.380(*)

0.15 0.584(**)

0.665(**)

0.510(**)

Pb 0.615(*)

�0.734(**)

0.698(**)

0.43 0.699(**)

0.49 �0.34 0.54 0.13 0.53 0.876(**)

�0.702(**)

0.836(**)

0.618(**)

0.827(**)

0.20 0.15 0.26 0.468(*)

0.19

Ti 0.43 �0.34 0.675(*)

0.751(**)

0.579(*)

0.17 0.03 0.26 0.07 0.24 0.30 �0.01 0.34 0.643(**)

0.30 0.13 0.14 0.34 0.409(*)

0.26

Zn 0.43 �0.20 0.45 0.46 0.41 0.50 �0.27 0.55 0.18 0.53 0.860(**)

�0.694(**)

0.784(**)

0.597(**)

0.802(**)

0.23 0.17 0.22 0.24 0.18

Fe 0.890(**)

�0.624(*)

0.912(**)

0.572(*)

0.941(**)

0.03 0.59 0.14 0.32 0.12 0.780(**)

�0.498(*)

0.747(**)

0.580(**)

0.725(**)

0.712(**)

�0.08 0.784(**)

0.885(**)

0.727(**)

**. Correlation is significant at the 0.01 level(2-tailed).*. Correlation is significant at the 0.05 level(2-tailed).Na ¼ 13, Nb ¼ 10, Nc ¼ 19, Nd ¼ 29.

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Fig. 4. Scatter diagram of cfd% and cARM/SIRM in the Lanzhou street dusts.

Table 5Statistical results of principal component analysis.

a. Total variance explained

Total % of Variance Cumulative %

Initial Eigenvalues8.944 38.887 38.8873.354 14.581 53.4682.495 10.847 64.3151.881 8.179 72.4941.453 6.318 78.813

Extraction Sums of Squared Loadings8.944 38.887 38.8873.354 14.581 53.4682.495 10.847 64.3151.881 8.179 72.4941.453 6.318 78.813

b. Component matrixesa for selected magnetic properties and elementconcentrations in Lanzhou Street dust

Component

C1 C2 C3 C4 C5

SIRM 0.938 �0.021 �0.149 �0.112 0.252SOFT 0.911 �0.023 �0.201 �0.14 0.264cLF 0.872 0.065 �0.255 �0.135 0.224HIRM 0.767 �0.207 0.207 0.008 0.392S�300 0.557 0.249 �0.577 �0.107 �0.251cfd% �0.48 �0.324 0.521 0.053 0.293Fe 0.892 �0.146 0.276 �0.024 0.08K �0.784 �0.056 0.381 �0.245 �0.1Pb 0.739 �0.084 0.298 0.099 �0.452Zn 0.676 0.158 0.08 0.207 �0.422Cu 0.673 �0.191 �0.043 0.121 �0.459As 0.669 �0.172 0.457 0.082 �0.371Ba 0.664 �0.211 �0.007 �0.424 0.219Mn 0.662 �0.411 0.368 �0.292 0.017Cr 0.018 0.871 0.043 0.344 �0.004Ni 0.436 0.714 0.165 0.385 0.227Al 0.185 0.642 0.52 0.162 0.259Bi 0.134 0.588 0.158 0.025 �0.025Mg �0.457 �0.492 0.419 0.286 0.023Na 0.305 �0.412 �0.325 0.351 �0.002Ti 0.518 0.104 0.657 �0.134 �0.082Ca �0.147 �0.473 �0.123 0.688 0.111Si �0.566 0.358 0.065 �0.654 �0.188

Extraction method: Principal Component Analysis.a 5 components extracted.

G. Wang et al. / Atmospheric Environment 46 (2012) 289e298 295

components are considered which account for almost 80% of thetotal variance (Table 5a). The initial component matrix for the dataset indicates that SIRM, SOFT, cLF, HIRM, Fe, Pb, As, Mn. Zn, Cu andBa are associated, displaying high values in the first component(C1), while Cr, Ni, Al and Bi are relative higher in the secondcomponent (C2). Ti and cfd% show the highest values in the thirdcomponent (C3), though, alongside them, As, Al andMg also displayrather high values.Ca shows the only high positive value in thefourth component (C4). All values in the fifth component (C5) arerelatively low.

Fig. 5. Scatter plot of cfd% and SIRM in the Lanzhou street dusts.

4. Discussion

4.1. Spatial variations in magnetic properties

Fig. 4, based on Dearing et al. (Dearing et al., 1997) plotting cfd%versus cARM/SIRM characterizes virtually all the samples as coarserthan SD, as do the consistent parallels between cARM/cLF values andthose for cfd% and cARM/SIRM (King et al., 1982) The plot recordsmainly low cfd% values, with the exception of isolated samples fromthe Qilihe District. The main peak in cfd% from the eastern edge of

the Qilihe District (Fig. 2) corresponds to minimum ferrimagneticconcentrations, as well as with minimum values for Pb, Cu and Zn.It records the least contaminated site. Three other samples fromQilihe and one from Anning also show higher cfd% values andsuggest that there are scattered areas of reduced contamination inthe central parts of the transect. These four samples are all rela-tively more distant from the main through road. They record theregional input of dust derived from eroding soils and from arid andsemi-arid areas to the north.

Fig. 5 shows some distinction between samples from the twomain industrial zones, Xigu and Chengguan, in terms of ferrimag-netic grain size. Fig. 6 shows that there is also some distinctionbetween the two main industrial areas in terms of the balancebetween anti-ferromagnetic minerals (mainly hematite) andferrimagnetic minerals. The magnetic mineral assemblages in mostof the Chengguan samples appear to be relatively richer in fineferrimagnetic grains, as well as in anti-ferromagnetic minerals.

4.2. Indications of pollution sources and source types

Obtaining samples of emissions from specific sources has notproved possible in the present investigation. Because of the

Fig. 6. Scatter diagram of cLF and S�300 in the Lanzhou street dusts.

G. Wang et al. / Atmospheric Environment 46 (2012) 289e298296

complex and multiple natures of dust sources, linking the samplecharacteristics to sources is therefore less than specific. Neverthe-less, there are associations and spatial variations that providepointers to source locations and distinctions. We first explore theseusing Principal Component Analysis, and then outline in moredetail specific magnetic propertyeelement concentration linkages.

4.2.1. Principal Component Analysis (PCA)PCA methods have been widely used in geochemical applica-

tions to identify pollution sources and to apportion natural andanthropogenic contributions.

Table 5b and Fig. 7 indicate that the magnetic concentrationindicators cLF, SIRM, SOFT, and HIRM, as well as S�300 are linked torelatively greater hematite contributions, along with As, Ba, Mn, Cu,Pb, Fe and Ti are clustered together. Generally, common sources ofAs in particulates are coal burning and other fossil fuel combustionprocesses (Yang et al., 1987). Vehicle exhausts and industrial fumes

Fig. 7. Scatter plot of loadings for principal component 1, 2 and 3.

are common sources of lead (De Miguel et al., 1997; Li et al., 2001).Coal burning is also acknowledged as a source of lead pollution(Zhang et al., 2006). Enrichment Factors (Sinex and Wright, 1988)are calculated as the quotient of the element to Al ratio in thesample over the element to Al ratio in the Upper Continental Crust(Taylor and McLennan, 1995). The high Enrichment Factors (EF)above mean crustal values (Sinex and Helz, 1981) for As, and Pb are19.55 and 7.83 respectively, which is consistent with their anthro-pogenic origin. In addition, the high values of cLF, SIRM, SOFT thatare linked to these confirm that these high element concentrationsarise from high temperature combustion processes. Therefore, thefirst component can be characterized as an “anthropogeniccombustion” factor.

The second component includes Cr, Al, Bi, and Ni. Cr, Bi and Ni aremainly used for metal alloy, chrome and ceramic manufacture(Madany et al., 1994; www.trencome.com/minormetalsproducts.htm). Nickel or Manganese and chromium are used in the manufac-ture of stainless steel (Al-Rajhi et al., 1996; Banerjee, 2003). Thecharacteristics of the second component are therefore also indicativeof a dominantly anthropogenic origin. The consistent offset in theprincipal component scores (Fig. 8) between the Xigu and Chengguansamples parallels the differences in magnetic mineralogy and grainsize (Figs. 5 and 6) and further reinforces the view that the sources ofcombustion-linked heavymetals differ in type between the twomainindustrial districts.

Ti, Mg, Al and K, like cfd%, have higher values in Component 3than in the other Components. This association between thecommon lithogenic elements and the main magnetic indicator offine grains typical of weathered soils rather than combustion-linked particulates reinforces the view that this componentmainly reflects remote, non-contaminated sources.

Comparison of Factor 1, 2 and 3 scores among 52 samples inXigu, Anning and Chengguan using statistical analysis of variance(ANOVA) test shows that the differences are significant at the 0.05level, with F values of 5.31, 35.40 and 3.63, and P values of 0.01,3.07E-10 and 0.03 respectively. The high score for principalcomponent 3 in Anning is consistent with the inferred minimalpollution input to this district.

4.2.2. Associations between magnetic properties and elementsCorrelations between total iron and the various magnetic

concentration indicators are all significant both at the wholetransect and district level, with r2 values almost all above 0.7.

Fig. 8. Scatter plot of principal component 1 and 2 scores for the Lanzhou Street dusts.

G. Wang et al. / Atmospheric Environment 46 (2012) 289e298 297

Magnetic oxides, mainly hematite, must form a significantpercentage of the total iron throughout the transect. At the wholetransect level, there are highly significant positive correlationsbetween magnetic concentrations and those for As, Cu, Mn, Ni, Pband Zn, confirming that much of the heavy metal contaminationacross the whole region is linked to particulate emissions resultingfrom combustion processes that also generated iron oxideimpurities.

Some of the distinctive characteristics are noted below ona district-by-district basis:

Xigu: This is the main industrial district. The Ni and Crconcentrations which peak in this district, are strongly correlatedwith each other (r2 ¼ 0.835), as well as with high HIRM values. Bycontrast, the main variations in Fe, Pb and Mn, which correlatestrongly with each other, are more strongly correlated with ferri-magnetic concentration indicators. These data suggest at least twocontrasted sources of combustion related, particulate-associatedheavy metal contamination in the area. In addition to two largechemical and petroleum industrial sites in the Xigu District, there isalso a coal-fired electrical power generation plant and an aluminummanufacturing plant.

Anning: In addition to the Fe and Pb linkages noted for Xigu, Cuconcentrations are also correlated with magnetic concentrations.Metal concentrations are, however, generally quite low and thereare no indications of separate and distinctive sources in this district.Anning District is clearly the one least polluted by anthropogenicemissions.

Qilihe: As well as containing samples with minimal indicationsof particulate pollution, this district also includes a group ofsamples on the western edge with the highest Zn concentrations ofany along the transect. These are correlated mainly with ferri-magnetic indicators (r2 ¼ 0.784e0.860) and constitute strongevidence for a significant and distinctive source of particulatepollution in this district. In other respects, the correlationsresemble those for neighboring Chengguan.

Chengguan: This is the main commercial and industrial district,with another coal-fired electrical power generation plant andrelative intense road traffic. As, Cu, Mn, Ni, Pb, Ti and Fe are allsignificantly correlated with each other (r2 ¼ 0.439e0.873) andmost are also correlated with ferrimagnetic concentrations, thoughPb and Ti are significantly correlated only with HIRM. The evidenceis consistent with multiple and diverse sources in this district. Znvalues are not notably high and they are not significantly correlatedwith any magnetic concentration indicators.

In summary, the combination of magnetic and elementconcentration data give clear indications of several distinct and, tosome degree contrasted sources of heavy metal contaminationlinked to particulate emissions.

5. Conclusions

The magnetic properties of the street dusts are dominated byrelatively coarse ferrimagnetic minerals though the presence ofa hard remanence component throughout confirms that hema-tite is likely to be the most abundant magnetic mineral. All theindicators of magnetic concentrations (cLF, SIRM, ‘Soft’ IRM,HIRM and cARM) show significant differentiation between thefour districts studied. They have maximum values in the twomain industrial and business districts, Xigu and Chengguan. TheAnning district which, unlike the others is on the north side ofthe Yellow river has consistently lower values and some sampleshave magnetic properties indicative of more remote, non-industrial sources in the arid areas to the north. Samples fromthe eastern border of the Qilihe district also have rather lowmagnetic concentrations.

There are some distinctions in magnetic properties and inmagnetic-heavy metal linkages between two major industrialareas. These may be of value in detecting the main source of heavymetal pollution given that samples from sources themselves are notavailable. Geochemical studies show that Fe, Al, Cu, and Zn gener-ally have the highest concentrations of all the heavy metals inLanzhou street dust samples.

Correlations between the total iron and the various magneticconcentration indicators are all significant both at the wholetransect and district levels, suggesting that iron oxides fromindustrial and urban sources make a major contribution to totaliron concentrations. Moreover, the highly significant positivecorrelations betweenmagnetic concentrations and those for As, Cu,Mn, Ni, Pb and Zn, confirm that much of the heavy metalcontamination across the whole region is linked to particulateemissions resulting from combustion processes.

The combination of magnetic and element concentration datapoint to several distinct and, to some degree contrasted sources ofheavy metal contamination linked to particulate emissions. Thedistrict-by-district correlations indicate that the general correla-tions for the whole study area hide considerable regional diversity.Combined magnetic and geochemical studies of street dusts whichact as a sink for industrial and vehicle-generated pollutants and areeasily collected, provide potentially useful information in urbanpollution studies, both in terms of the volume and distribution ofpollutants, and the spatial patterns of potentially source-diagnosticcharacteristics.

Acknowledgments

We are grateful to Prof LiZhong Yu for his precious help. Theauthors thank Prof. John Shaw and Jan Bloemedal for their help inLiverpool. This work was supported by National Nature ScienceFoundation of China (No. 40571147, No. 41001331 and No.40830105).

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