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Nanayakkara Mummullage, Sandya Wasanthi, Egodawatta, Prasanna,Ayoko, Godwin, & Goonetilleke, Ashantha(2016)Use of physicochemical signatures to assess the sources of metals in ur-ban road dust.Science of the Total Environment, 541, pp. 1303-1309.

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https://doi.org/10.1016/j.scitotenv.2015.10.032

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USE OF PHYSICOCHEMICAL SIGNATURES TO ASSESS THE SOURCES OF METALS IN URBAN ROAD DUST

Sandya Mummullage, Prasanna Egodawatta*, Godwin A. Ayoko, Ashantha Goonetilleke

Science and Engineering Faculty, Queensland University of Technology (QUT), G.P.O. Box 2434, Brisbane, QLD 4001, Australia

*Corresponding author: E-mail: p.egodawatta@qut,edu.au; Tel: +61 7 3138 4396; Fax: +61 7 3138 1170

Abstract

Road deposited dust is a complex mixture of pollutants derived from a wide range of sources. Accurate identification of these sources is seminal for effective source-oriented control measures. A range of techniques such as enrichment factor analysis (EF), principal component analysis (PCA) and hierarchical cluster analysis (HCA) are available for identifying sources of complex mixtures. However, they have multiple deficiencies when applied individually. This study presents an approach for the effective utilisation of EF, PCA and HCA for source identification, so that their specific deficiencies on an individual basis are eliminated. EF analysis confirmed the non-soil origin of metals such as Na, Cu, Cd, Zn, Sn, K, Ca, Sb, Ba, Ti, Ni and Mo providing guidance in the identification of anthropogenic sources. PCA and HCA identified four sources, with soil and asphalt wear in combination being the most prominent sources. Other sources were tyre wear, brake wear and sea salt.

Keywords

Urban road dust, Source identification, Enrichment factor analysis, Principal component analysis, Hierarchical cluster analysis, Stormwater quality

1. Introduction

Road dust contains potentially toxic pollutants including metals and hydrocarbons that can negatively impact urban waterway ecology, by washing-off with stormwater runoff. Metals in road dust are of particular concern due to their toxicity, bioavailability and non-degradable characteristics. In this context, an accurate identification of the sources of metals in road dust is of vital importance for the formulation of effective pollution mitigation strategies.

Source identification is a complex process that requires a systematic analytical approach. A range of analytical techniques have been used in the past for source identification with varying success, including enrichment factor (EF) analysis and multivariate analytical techniques. EF analysis is primarily used for discriminating anthropogenic metal sources from natural sources in sample matrices such as soils, sediments and road dust (Binta et al., 2013; Blaser et al., 2000; Mohammed et al., 2012; Reimann and de Caritat, 2005).

In general, EF analysis does not provide comprehensive identification of sources in a complex source mix. This is due to the limited capabilities of the technique for discriminating between multiple sources. In this context, multivariate data analytical techniques such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) perform better due to their enhanced capabilities to distinguish multiple sources. However, both PCA and HCA are only capable of clustering a set of elements into groups based on their statistical

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variances. Labelling these clusters of elements as specific sources requires prior knowledge of physicochemical signatures of the potential sources in a particular microenvironment.

In this context, it is hypothesised that the integrated use of a suite of complementary analytical tools to eliminate their individual deficiencies can provide a robust approach to source identification. In this study, EF analysis, PCA and HCA were employed as an integrated analytical approach for source identification of the complexity of sources contributing to road dust. The approach adopted in this study can be utilised for identification of pollutant sources contributing to complex mixtures. The study outcomes can be utilised in practice for developing robust source-oriented pollution mitigation strategies to safeguard urban water environments. 2. Materials and methods 2.1. Study sites

The study was conducted in the Gold Coast region, South East Queensland, Australia. Four suburbs namely, Surfers Paradise, Benowa, Nerang and Clearview Estate-Nerang were selected for sampling as shown in Figure 1. Selected road sites for sampling consisted of different land uses (Commercial, Industrial and Residential) and also represented significant variation in soil characteristics as the suburbs stretched from the coastline to the inland. Altogether, sixteen sites were selected for sampling, consisting of four road sites from each suburb. The different road sites in each suburb have variable traffic characteristics specific to that suburb.

Figure 1: Map of the selected study sites

2.2. Sample Collection

Dust samples were collected from 3m2 road surface plots using a dry and wet vacuuming system. Details of the vacuum system used are available in Gunawardana et al. (2012) and Mahbub et al. (2010). Samples were collected in two sampling episodes from each of the 16

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sites, representing two antecedent dry periods. To obtain physicochemical signatures to support source identification, samples from potential pollutant sources such as roadside soil, tyre dust, brake dust and asphalt wear were collected. These potential sources are reported in research literature as the most critical sources of metal pollution in road dust (Adachi and Tainosho, 2004; Chang et al., 2009). Roadside soil samples were collected at a depth of 5-10cm from the ground surface such that they are not contaminated by other sources and expected to be present in road dust as a source of geogenic metals. Tyre wear was collected by chipping-off from tyre thread and brake wear was collected from the rim of a front brake lining (Adachi and Tainosho, 2004). Asphalt wear was collected by road milling (Kennedy and Gadd, 2000). Each source sample was a composite sample formed from three samples taken from randomly selected tyres, brake rims and road plots. 2.2 Laboratory Analysis

All the samples were tested for metal elements, namely: Li, Na, Mg, Al, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, Rh, Pd, Cd, Sn, Sb, Ba, Pt and Pb. The metal elements were carefully selected such that they represent tracers of potential sources to facilitate clear differentiation between sources. Testing was based on EPA 200.8 method (EPA, 1994). Quality control/assurance procedures found the percentage recovery as 90–115%. Analysis of replicates yielded a relative standard deviation of less than 15% which is within the error limits for road dust samples (Mahbub, et al., 2010).

Morphology of the collected samples was investigated using a Quanta 3D FIB (Focused Ion Beam) scanning electronic microscope (SEM). Samples used for SEM were coated with ultrathin layer of gold using Leica EM SCD005 gold coater to a thickness of 10nm, to avoid the accumulation of electrostatic charge. 2.3 Analytical tools

Enrichment factor analysis (EF), principal component analysis (PCA) and hierarchical cluster analysis (HCA) was employed in this study. EF was used to determine the degree of anthropogenic influence on road dust samples. Enrichment of the given metal elements in road dust was determined relative to the metal concentrations of roadside soil so that the anthropogenic influence of dust in comparison to adjacent soil can be determined (Mohammed et al., 2012). EF values >1 typically indicate that the sample is enriched from anthropogenic sources. The degree of anthropogenic influence was further classified as deficient to minimal (EF<2), moderate (EF=2-5), significant (EF=5-20), very high (EF=20-40) and extremely high (EF>40) (Lu et al., 2009; Yongming et al., 2006). More details on enrichment factor analysis are provided in the Supplementary Information.

Secondly, PCA was undertaken to identify the exact sources of metals. PCA is a data-reduction technique that aims to reduce a larger set of variables to a smaller set of independent 'artificial' variables called principal components (PCs). PCs are formed so that they comprise of metal elements with similar variances. This feature was utilised as the primary tool for source identification. The number of possible PCs that can be produced in PCA is equivalent to the number of variables considered. PCs are generally formed in the order of highest associated variance to the lowest so that the user the can choose the most influential set of PCs. The eigenvalue ≥ 1 criterion (also referred to as the Kaiser Normalisation) was used for the determination of the optimal number of principal components (PCs) which are considered as independent sources (Gunawardena et al., 2014).

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The PCs with eigenvalue less than one were excluded from the analysis considering their contribution as sources, as minor.

HCA was applied to confirm the outcomes of PCA. HCA is typically used for clustering complex data sets and is considered as a complementary technique to PCA. The key component of HCA is the repeated calculation of distance measures between objects (or variables), and between clusters, once objects (or variables) begin to be grouped into clusters (Brady, et al., 2014). 3. Results and discussion 3.1. Signatures of potential sources

Metal composition profiles of potential sources were first examined to identify signatures. The composition profiles of the roadside soils, asphalt, tyre and brake dust are presented in Figure 2. As shown in Figure 2, elevated concentrations for elements namely, Al, Ca and Fe are followed by Na, Mg, K, Ti, Mn, Ba and Pb for roadside soils. Pt, Pd and Rh were not detected while metals such as Cr, Cu, Zn and Cd were detected in minor quantities.

As shown in Figure 2, Zn registered the highest concentration in tyre samples, agreeing with the finding by Kennedy and Gadd (2000). Other metal elements such as Na, Ca, Al, K, Ti and Fe were detected, although their concentrations were an order of magnitude lower than that of Zn.

Figure 2: Average concentrations of metals in roadside soil, asphalt, tyre and brake dust

(logarithm scale)

Most of the metals tested, except Rh, Pd, Pt and Cd, were detected in the brake dust. Furthermore, Ca, Ti, Fe, Cu and Ba were found to be the signature metals in the brake dust while Zn, Al, Sb, Mn, Na and Mg were detected at concentrations an order of magnitude less than the dominant metals. Though Cd was not detected in this study, McKenzie et al. (2009) noted that the Cd is associated with brake dust due to its use as plating to prevent brake pad corrosion.

As shown in Figure 2, Na, Mg, Al, K, Ca, Ti and Fe are the signature elements detected in the asphalt sample. V, Cr, Co, Ni, Cu, Zn and Pb were also found in low concentrations in the asphalt. Average concentrations of metals in road dust for each suburb are presented in the

1.0E-06

1.0E-04

1.0E-02

1.0E+00

1.0E+02

Li Na Mg Al K Ca Ti V Cr Mn Fe Co Ni Cu Zn Mo Cd Sn Sb Ba Pb

Conc

entr

atio

n (m

g/kg

x 1

03 )

Roadside soilAsphaltTyreBrake dust

5

Supplementary information (Table S1). As seen in Table S1, average concentrations are different from one suburb to another, suggesting the influence of land use activities and soil characteristics specific to the corresponding suburbs. Additionally, high standard deviation in the data values suggests that there is a considerable variation in metal concentrations even within the same suburb.

3.2. Source identification of metals in road dust 3.2.1. Enrichment factor analysis

Enrichment factor (EF) analysis was first undertaken to demonstrate the anthropogenic metal influence on the dust samples. For EF analysis, roadside soil collected at each site was considered as the reference sample while Mn was considered as the reference element. The approach adopted for selecting Mn as the reference metal is discussed in the Supplementary information.

EFs of 21 metals (out of 24 elements) were determined and the outcomes in the form of box-whiskers plot i shown in Figure 3. As Pt, Rh and Pd were not detected in any of the roadside soil samples, they were deemed to be originating purely from anthropogenic sources. It has been reported in research literature that Pd, Rh and Pt are released from catalytic converters in vehicles (Lim et al., 2006;Palacios et al., 2000).

Figure 3: Fundamental statistical parameters for enrichment factor (EF) analysis (Note: EF was calculated using soil as reference sample and Mn as reference material. Fundamental statistical parameters were derived from the 32 sample dataset)

As evident in Figure 3, EFs for Na, Mg, K, Ca, Cu, Zn, Cd, Sb and Ba are higher than 1 for all road dust samples indicating a dominant non-soil influence. Among analysed metals, Cu (average EF = 21) and Na (average EF = 33) recorded very high enrichment. Average EFs of Zn (8.6), Cd (19.2) and K (5.6) indicate that these metals are significantly enriched during build-up. Metals such as Mg (2.4), Ca (4.8), Ti (2.4), Ni (2.4), Mo (2.0) Sn (6.3), Sb (4.2) and Ba (3.4) exhibited moderate enrichment while the rest of the metal enrichments were minimal. This suggested the need for additional analytical techniques to identify specific non-

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soil sources, while indicating that the metal elements Na, Cu, Cd, Zn, Sn, K, Ca, Sb, Ba, Ti, Ni and Mo merit special focus in such identification. 3.2.2. Principal component analysis

PCA was used as the primary tool for source identification of non-soil sources. Prior to PCA, the data set was transformed to dimensionless standardized form by subtracting the mean concentration of each metal from metal concentration in each sample and dividing by the standard deviation of the concentrations so that the mean concentration of each metal element was equal to zero and standard deviation equal to one. This standardisation allowed variables to cluster together based on their variance. As PCA is highly sensitive to outliers (Guo, et al. 2004a; 2004b), Hotelling T2 test was used to identify outliers (Settle et al., 2007) using SIRIUS 2000 software (Sirius, 2008). Accordingly, four outliers were removed from the data matrix.

Four principal components with eigenvalue greater than one were extracted. The extracted principal components were subjected to Varimax rotation in order to obtain an easily interpretable matrix as presented in Table 1 (Singh et al., 2004). The analysis was performed using SPSS software (SPSS, 2012).

Component 1

As evident from Table 1, component 1 explains 30.7 % of the total data variance showing high component loadings for Al, V, Mn and Fe while having moderate loadings for Mg, Cr, Co, Zn, Ba and Pb. According to Section 3.1, Mg, Al, Mn, Fe and Pb were found to be in appreciable concentrations in roadside soil. V, Cr, Co, Zn and Ba usually have anthropogenic sources of origin and show a close association with asphalt wear as noted in Section 3.1. In Section 3.1, the signature elements for asphalt were identified as Mg, Fe and Al while Co, Cr, Zn, Ba and V are also present in minor quantities. Moreover, as pointed out by Adachi and Tainosho (2004), road paint can also be a source of Pb and Cr. This suggests that component 1 is primarily associated with soil and asphalt wear. Possibility of associating vehicle emissions to component 1 was rejected due to the absence of elements such as Ni and the presence of element such as Pb. Ni is a signature element of vehicle emissions, while Pb is currently not in use as a fuel additive.

Component 2

Component 2 is characterised with Li, Cr, Ni, Zn, Mo, Sb and Ba explaining 19.3% of the total data variance. Presence of Zn, the signature element for tyres, suggests the association of tyre wear as a source. Zn is used as a vulcanizing agent (zinc oxide) and Sb is used as a colorant (antimony pentasulphide) in tyre production (Kennedy and Gadd 2000; Councell et al. 2004). Other than Zn and Sb, minor amounts of Na, K, Al, Ba, Ca, Cu, Fe are also present in tyre wear with variable concentrations depending on the manufacturing process adopted (Kennedy and Gadd, 2000). It is also commonly known that Sb and Ba are tracer elements of brake wear (Gietl et al., 2010). However, component 2 was considered to be a tyre wear source due to the lack of association with Cu, which is a major tracer for brake wear (Thorpe and Harrison, 2008)

Component 3

Component 3 is associated with high loadings of K, Cu, Cd, Sn and Sb. Due to the presence of Cu and Sb, component 3 was considered to be primarily brake wear (Adachi and Tainosho,

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2004). Other than Cu and Sb, Cd is also used as plating material to prevent brake pad corrosion (McKenzie et al., 2009).

Table 1: Rotated component matrix for refined metal concentration data set

Metal

Component

1 2 3 4 Li 0.637 Na 0.897 Mg 0.523 0.657 Al 0.915 K 0.780 Ca

Ti V 0.886 Cr 0.681 0.622 Mn 0.862 Fe 0.866 Co 0.740 Ni 0.730 Cu 0.699 Zn 0.524 0.626 Mo 0.810 Cd 0.781 Sn 0.779 Sb 0.604 0.544 Ba 0.634 0.608 Pb 0.732 % of variance 30.7 19.3 15.2 11.1 Cumulative % 30.7 50.0 65.2 76.3

Only component loading ≥0.5 is displayed

Component 4

Component 4 is associated with high loadings of Na and Mg. These metals typically originate from sea salt (Friend and Ayoko, 2009; Gong et al., 1997). Therefore, component 4 can be assigned as sea salt. As the selected sites for road dust sampling were approximately within 12 km from the coastline, the presence of sea salt in road dust is justifiable. 3.2.3. Hierarchical cluster analysis

Accuracy of the identified metal sources using PCA was tested using HCA. The HCA dendrogram, using Ward’s linkage and Euclidean distance method of clustering (Singh et al., 2004), is given in Figure 4. The same data set used for PCA after the exclusion of outliers was used for the HCA.

Four clusters were identified by HCA. Cluster 1 consisted of metals found in the soil and asphalt wear, namely; Al, V, Fe, Cr, Mn, Co and Pb. Cluster 2 was characterised by Mg, Ca, Na and Ni indicating the major source associated with it as sea salt. Cluster 3 comprised of K,

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Cd, Cu and Sn, indicating brake wear origin while Cluster 4 comprised of Zn, Ti, Sb and Mo of tyre wear origin. Accordingly, the HCA provides four consistent source categories in line with PCA results. Therefore, four sources, namely, soil and asphalt wear, tyre wear, brake wear and sea salt were considered as key natural and anthropogenic sources of metals in road dust.

Overall, the combined use of PCA and HCA enable identification of more major sources compared to EF analysis. However, knowledge generated from EF analysis was important in determining non-soil sources and played a critical part in assigning sources for components derived from PCA.

Figure 4: Dendrogram of clusters of metals

3.2.4. Particle Morphology

Confirmation of analytical outcomes with the help of visual evidence is important in enhancing confidence of identified sources. In this regard, Figure 5(a) to (e) are SEM images of particles in road deposited dust, soil, tyre, brake dust and asphalt, respectively. Figure 5 enables the comparison of the morphology of potential sources and road deposited dust. As evident in Figure 5(b), soil particle size is comparatively larger and irregular than tyre wear and brake wear, while similar morphology can be observed from Figure 5(e) for asphalt. This confirms that soil and asphalt wear are similar in terms of morphology of the particles generated. As seen in Figure 5(a), particles with large diameters and irregular shapes are dominant in road deposited dust, confirming the soil and asphalt wear origin. Identification of tyre and brake dust using SEM image is diffult due to their fineness. Identification of sources using SEM images can be further complicated by the involvement of minor sources such as land use related emissions and long-range windblown dust. The shapes and the characteristics of the particles observed in this study are well matched with the outcomes from past research studies (for example, Adachi and Tainosho, 2004; Gualtieri et al., 2005).

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Figure 5: SEM images (a) soil (b) tyre (c) brake dust (d) asphalt (e) road dust

4. Conclusions

This study provides an approach for the effective utilisation of different analytical tools such as enrichment factor analysis, principal component analysis and hierarchical cluster analysis, while overcoming deficiencies associated with each specific technique so that accurate and reliable source identification can be accomplished. Following are the key conclusions derived from the study.

• EF analysis confirmed the anthropogenic or non-soil origin of Na, Cu, Cd, Zn, Sn, K, Ca, Sb, Ba, Ti, Ni and Mo. There metals are the critical indicators in determining non-soil sources.

• Soil and asphalt wear had similar data variances and clustered as a common source. This indicates the similarity of soil and asphalt wear contributions to road dust in terms

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of chemical composition. Soil and asphalt wear particles are also similar in terms of particle morphology.

• Tyre wear, brake wear and sea salts were identified as the other major sources of metals in road dust. Presence of tyre wear and brake wear in road dust as a major metal source suggest the critical involvement of traffic activities in urban stormwater pollution.

• Presence of sea salts in road dust indicates atmospheric deposition as a pathway for metal accumulation on road surfaces.

• Scanning electronic microscope (SEM) images of particle morphology indicate similarity of particles from soil and asphalt sources. SEM images also indicate the involvement of minor sources such as land use related emissions and long-range windblown dust.

Supplementary information The Supplementary information provides further details of the enrichment factor analysis and the metal concentrations in the road dust collected from study sites. References Adachi, K. and Tainosho, Y. (2004). Characterization of heavy metal particles embedded in tire dust. Environment International, 30(8), 1009-1017.

Binta, H. A., Kabir, S., Selim Reza, A. H. M., Nazim Zaman, M., Ahsan, A. and Rashid, M. (2013). Enrichment factor and geo-accumulation index of trace metals in sediments of the ship breaking area of Sitakund Upazilla (Bhatiary–Kumira), Chittagong, Bangladesh. Journal of Geochemical Exploration, 125(0), 130-137.

Blaser, P., Zimmermann, S., Luster, J. and Shotyk, W. (2000). Critical examination of trace element enrichments and depletions in soils: As, Cr, Cu, Ni, Pb, and Zn in Swiss forest soils. Science of The Total Environment, 249(1–3), 257-280.

Chang, S.-H., Wang, K.-S., Chang, H.-F., Ni, W.-W., Wu, B.-J., Wong, R.-H. and Lee, H.-S. (2009). Comparison of source identification of metals in road-dust and soil. Soil and Sediment Contamination: An International Journal, 18(5), 669-683.

Councell, T. B., Duckenfield, K. U., Landa, E. R. and Callender, E. (2004). Tire wear particles as a source of zinc to the environment. Environmental Science & Technology, 38(15), 4206-4214.

EPA. (1994). Method 200.8, Trace elements in waters and wastes by inductively coupled plasma-mass spectrometry. Revision 5.4. U. S. Environmental Protection Agency, Ohio.

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Gietl, J. K., Lawrence, R., Thorpe, A. J. and Harrison, R. M. (2010). Identification of brake wear particles and derivation of a quantitative tracer for brake dust at a major road. Atmospheric Environment, 44(2), 141-146.

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Gualtieri, M., Andrioletti, M., Mantecca, P., Vismara, C. and Camatini, M. (2005). Impact of tire debris on in vitro and in vivo systems. Particle and Fibre Toxicology, 2(1), 1.

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McKenzie, E. R., Money, J. E., Green, P. G. and Young, T. M. (2009). Metals associated with stormwater-relevant brake and tire samples. Sci Total Environ, 407(22), 5855-5860.

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Settle, S., Goonetilleke, A. and Ayoko, G. A. (2007). Determination of surrogate indicators for phosphorus and solids in urban stormwater: Application of multivariate data analysis techniques Water, Air, & Soil Pollution, 182(1), 149-161.

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SUPPLEMENTARY INFORMATION

Use of Physicochemical Signatures to Assess the Sources of Metals in Urban Road Dust

Sandya Mummullage, Prasanna Egodawatta*, Godwin A. Ayoko, Ashantha Goonetilleke

Science and Engineering Faculty, Queensland University of Technology, G.P.O. Box 2434, Brisbane, QLD 4001, Australia

*Corresponding author:

E-mail: p.egodawatta@qut,edu.au; Tel: +61 7 3138 4396; Fax: +61 7 3138 1170

Enrichment Factor Analysis

Enrichment factor (EF) analysis is widely used to discriminate natural and anthropogenic sources of metal elements (Binta, et al., 2013; Blaser, et al., 2000; Reimann and de Caritat, 2005). In this study, Enrichment of the given metal elements in road dust was determined relative to the metal concentrations of roadside soil so that the anthropogenic influence in dust in comparison to adjacent soil can be determined (Mohammed, et al., 2012).

As shown in Eq. (1), EFs are determined by normalising a metal concentration of a sample against concentration of a reference metal in the background sample (Zoller et al., 1974).

EF = (Y/X)sample

(Y/X)Background (1)

Where: Y is the concentration of metal element of interest and X is the concentration of reference metal.

In this study, roadside soil was selected as the background sample whilst, the reference metal was selected after a careful analysis of variances due to its influential role on analytical outcomes. Metals such as Al, Fe, Mn have been commonly used as the reference metal for soil in past research studies (Binta, et al., 2013; Brady, et al., 2014; Loska, et al., 1997). Decision to select a particular metal as a reference is often based on its variability within the study domain. The data variability expression in the form of non-parametric coefficient of variance (CV*) deemed ideal for such decision making. The decision to select a reference metal would rely on the CV* calculated using Eq. (2) for the commonly used reference metals (such as Al, Fe, Mn), in comparison to CV* for common anthropogenic metals (such as Cu, Zn, Sn and Pb) (Zhou et al., 2007). The reference metal with lowest CV* in comparison to the anthropogenic metals was selected for the determination of EFs.

CV*=median{|xi-median(xi)|}

median(xi)×100 (%) (2)

Where: xi is the individual concentrations of metals in the data set.

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CV* for reference elements, Al, Mn and Fe were found to be 31%, 26% and 36%, respectively. These values were compared with CV* values for common anthropogenic metals, Cu (41%), Zn (37%), Sn (59%) and Pb (48%). It is commonly known that the variability of the reference metal greater than or as great as that of the elements of interest can result in EFs biased towards the distribution of reference metal itself than to that of elements of interest (according to Eq. 1). Based on this, Mn, which recorded the lowest CV* compared to anthropogenic metals, was selected as the reference metal in this study. By this selection, it was considered that the anthropogenic influence of Mn was minimal in road dust samples.

Primary data (metal concentrations in road dust) Average concentrations of metals in road dust for each suburb are shown in Table S1. As evident in Table S1, most dominant metals in road dust were Na, Mg, Al, K, Ca, and Fe. The same metals were found in abundance in roadside soil suggesting their origin. However, presence of metals such as Na and K in abundance suggests the contributions of other sources such as sea salt. Moderate to low levels of metal concentrations were observed for other metals. It could be seen that the average metal concentrations are different from one suburb to another, suggesting the influence of land use activities and soil characteristics specific to the corresponding suburbs. Additionally, high standard deviation suggests that there is a considerable variation of metal concentrations even within the same suburb. This was attributed to the site specific nature of the metal accumulation on road surfaces influenced by parameters such as traffic.

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Table S1: Average metal concentrations in road dust in different suburbs

Element Average metal concentrations ± Standard deviation (mg/kg )

Surfers Paradise (Commercial)

Nerang (Industrial)

Benowa (Mixed)

Clearview Estate (Residential)

Li 9 ± 6 12 ± 4 10 ± 4 8 ± 7

Na 9370 ± 4914 7487 ± 9949 8093 ± 5107 5001 ± 1646

Mg 7673 ± 3050 7156 ± 3410 8347 ± 3470 5891 ± 3923

Al 15731 ± 4665 15886 ± 8769 18679 ± 5874 17603 ± 18174

K 6602 ± 1596 6650 ± 3413 8388 ± 4311 6434 ± 3743

Ca 45027 ± 20506 38773 ± 16328

36511 ± 19539 21458 ± 6087

Ti 488 ± 218 785 ± 424 709 ± 405 568 ± 611

V 33 ± 11 31 ± 17 40 ± 18 39 ± 40

Cr 31 ± 14 42 ± 25 44 ± 21 37 ± 32

Mn 667 ± 280 666 ± 274 693 ± 243 784 ± 619

Fe 26034 ± 11913 28875 ± 15888

31226 ± 12613 36768 ± 32141

Co 18 ± 10 16 ± 6 18 ± 8 13 ± 10

Ni 27 ± 18 46 ± 22 45 ± 42 25 ± 19

Cu 842 ± 442 843 ± 742 1110 ± 377 738 ± 546

Zn 1759 ± 461 2305 ± 1483 2823 ± 1339 1188 ± 679

Mo 4 ± 2 7 ± 6 6 ± 3 9 ± 17

Rh 1 ± 1 2 ± 2 2 ± 1 3 ± 3

Pd 5 ± 3 7 ± 6 6 ± 4 15 ± 11

Cd 3 ± 5 3 ± 2 5 ± 4 4 ± 4

Sn 16 ± 10 17 ± 26 19 ± 12 18 ± 16

Sb 8 ± 4 9 ± 7 14 ± 7 9 ± 7

Ba 381 ± 99 465 ± 200 597 ± 220 954 ± 1788

Pt 1 ± 1 2 ± 2 2 ± 1 3 ± 3

Pb 208 ± 123 238 ± 325 321 ± 187 319 ± 534

References

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Blaser, P., Zimmermann, S., Luster, J. and Shotyk, W. (2000). Critical examination of trace element enrichments and depletions in soils: As, Cr, Cu, Ni, Pb, and Zn in Swiss forest soils. Science of The Total Environment, 249(1–3), 257-280.

Brady, J. P., Ayoko, G. A., Martens, W. N. and Goonetilleke, A. (2014). Enrichment, distribution and sources of heavy metals in the sediments of Deception Bay, Queensland, Australia. Marine Pollution Bulletin, 81(1), 248-255.

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Reimann, C. and de Caritat, P. (2005). Distinguishing between natural and anthropogenic sources for elements in the environment: regional geochemical surveys versus enrichment factors. Science of The Total Environment, 337(1–3), 91-107.

Zhou, F., Guo, H. and Liu, L. (2007). Quantitative identification and source apportionment of anthropogenic heavy metals in marine sediment of Hong Kong. Environmental Geology, 53(2), 295-305.

Zoller, W. H., Gladney, E. S. and Duce, R. A. (1974). Atmospheric Concentrations and Sources of Trace Metals at the South Pole. Science, 183(4121), 198-200.