The build-up dynamic and chemical fractionation of Cu, Zn and Cd in road-deposited sediment

Science of the Total Environment 532 (2015) 723–732

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

The build-up dynamic and chemical fractionation of Cu, Zn and Cd in road-deposited sediment Jin Zhang a,⁎, Pei Hua b,⁎, Peter Krebs a a b

Institute of Urban Water Management, Technische Universität Dresden, 01062 Dresden, Germany Chair of Water Supply Engineering, Institute of Urban Water Management, Technische Universität Dresden, 01062 Dresden, Germany

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• RDS adsorbed Zn and Cd was predominantly in unstable exchangeable fractions. • Zn and Cd could pose higher risks than Cu to aquatic biota over a longer ADP. • ADP had a strong influence on the metals' build-up processes. • Chemical fractionation of metals was to a less extent dependent on the land-use type.

a r t i c l e

i n f o

Article history: Received 4 March 2015 Received in revised form 14 June 2015 Accepted 19 June 2015 Available online xxxx Editor: D. Barcelo Keywords: Build-up dynamics Chemical fractionation Heavy metal Road-deposited sediment Stormwater management

a b s t r a c t This study investigates the build-up dynamics of heavy metals on impervious urban surfaces with different antecedent dry-weather periods (ADPs) and land-use types. Solid-phase concentration (mg/kg), surface load (mg/ m2), and chemical fractionation of Zn, Cu and Cd in bulk- and size-fractionated road-deposited sediment were determined. The inherent correlations among particle size distribution, ADP, land use, and chemical fractionation were analysed by hierarchical cluster analysis. Results show a clear build-up phenomenon of Cu and Zn at a city commercial centre and a highway area. Cd had complex build-up patterns. With regard to chemical fractionation, Zn and Cd could pose higher risks than Cu to aquatic biota after a longer ADP. Special attention should be paid to the significant risk in a rural area because of the high proportion of exchangeable chemical fractionation in terms of the unstable Cd component. Hierarchical cluster analysis indicates that ADP had a strong influence on build-up processes of sediments and associated metals. The metal contents were very dependent on the particle size distribution. However, the chemical fractionation of metals was dependent to a lesser extent on the land-use type. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Urban stormwater runoff affects water quality, water quantity, habitat and biological resources, public health, and the aesthetic appearance ⁎ Corresponding authors. E-mail addresses: [email protected] (J. Zhang), [email protected] (P. Hua).

http://dx.doi.org/10.1016/j.scitotenv.2015.06.074 0048-9697/© 2015 Elsevier B.V. All rights reserved.

of urban waterways (Sartor and Boyd, 1972; Strassler et al., 1999). As reported by several studies, urban runoff is a leading source of pollutants causing water quality impairment related to human activities in ocean shoreline, estuaries, rivers, lakes and so on (Lau and Stenstrom, 2005; Taylor and Owens, 2009; Loganathan et al., 2013; Fletcher et al., 2014). In Germany, urban stormwater runoff has been regarded as the largest pathway-related emission of several heavy metals to receiving

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J. Zhang et al. / Science of the Total Environment 532 (2015) 723–732

waters (Hillenbrand et al., 2005). Therefore, stormwater pollution is a critical challenge to the potential strategies of sustainable urban drainage systems and stormwater best management practices (Fletcher et al., 2014). Generally, stormwater pollutants undergo three primary processes: build-up, wash-off and transportation (Egodawatta et al., 2007). Compared with wash-off and transportation, less attention has been paid to pollutants' build-up processes on the urban surfaces. However, more recently, climate change has induced extended periods of high-intensity rainfall and rapid urbanization has induced changes of watershed hydrology, both of which boost stormwater pollution loads because of the washing away of road-deposited sediment (RDS) and its adsorbed pollutants (Shuster et al., 2005; Sundaray et al., 2011; Zhang et al., 2015). Therefore, RDS and its adsorbed pollutants have received considerable attention in recent years as a consequence of their adverse impact upon urban runoff and their propensity to act as a proxy for urban pollution levels (Taylor and Owens, 2009). Among such pollutants, heavy metals are of concern because of their high potential toxicity in various biological forms. Some of them are toxic even if their concentrations are very low and their toxicity increases with accumulation in the environment (Bradl, 2004; Dhanakumar et al., 2013). Therefore, aquatic environmental source control and stormwater pollution mitigation of metals should be based on the in-depth understanding of RDS-adsorbed heavy metals on urban impervious surfaces. In this context, Sartor and Boyd (1972) carried out one of the early studies regarding the total solid-phase concentration (mass of metals per unit mass of dry solid) of metals in RDS for stormwater pollution. However, the environmental and health effects of metals are not only related to their total solid-phase concentrations but also to their mobility and bioavailability in terms of their chemical fractionation (Tessier et al., 1979; Tack and Verloo, 1995; Rauret et al., 1999; Sundaray et al., 2011). Concerning this issue, Sutherland et al. (2012) determined metals' chemical fractionation in size-fractionated RDS. In their work, however, the pollutant build-up process over the antecedent dry-weather period (ADP, the length of time which has elapsed since the last substantial rainfall event) and the influence of urbanization were not taken into account. In fact, ADP is of particular importance to the pollutant build-up dynamic (Egodawatta et al., 2007; Wicke et al., 2012). It is widely accepted that the longer the RDS build-up time, the higher the mass of metals per unit surface area (mg/m2), referred to as surface load (Sartor and Boyd, 1972; Wicke et al., 2012; Egodawatta et al., 2013). The accumulation of metals in ecosystems has increased considerably in recent times because of urbanization-induced anthropogenic activities, e.g. traffic load increase (Lau and Stenstrom, 2005) and land-use change (Shuster et al., 2005; Dhanakumar et al., 2013). The quantity of pollutants per unit area delivered to receiving waters tends to increase with the degree of development in urban areas (Strassler et al., 1999), and/or with urbanization-induced changes of land-use type (Dhanakumar et al., 2013). Therefore, it would be interesting to know the build-up dynamics of metals' chemical fractionation on impervious urban surfaces, and the influences of ADP and land-use type on the apportioning of metals' chemical fractionation. Furthermore, particle size distribution is a crucial physical characteristic of RDS (Gunawardana et al., 2014; Zhang et al., 2015). In terms of stormwater management, RDS particle size distribution is related to the fluid transport theory (Bridge, 2009). It is well known that RDS with a finer size fraction cannot be efficiently removed by regular sweeping of the road surface but can be more easily retained and transported via runoff than larger particles (McKenzie et al., 2008). The finer particles can stay in suspension longer and be transported further by runoff than larger particles (McKenzie et al., 2008; Loganathan et al., 2013). Additionally, the contaminant literature suggests that there is a particle size control on metals' adsorption/desorption, biotic ingestion, and bioavailability (Tack and Verloo, 1995; Lau and Stenstrom, 2005; Sutherland et al., 2012). However, to date, there has

been less systematic examination of the mass percentage of metals' chemical fractionation in size-dependent RDS. These data have implications for a more efficient stormwater pollution management by reducing RDS-adsorbed metals to receiving waters via urban stormwater runoff. Therefore, the primary objective of the present study was to provide data to assist potential strategies of stormwater pollution mitigation and integrated catchment management to minimise the adverse impacts of RDS-adsorbed heavy metals on stormwater quality. To the best of the authors' knowledge, this is the first look at the build-up dynamics of metals' chemical fractionation in bulk- and size-dependent RDS samples for a stormwater pollution aspect. The detailed focuses were to (i) determine the metals' chemical fractionation in bulk- and size-dependent RDS samples over different ADPs, and (ii) analyse the influences of ADP, land use and particle size distribution on metals' build-up patterns and chemical fractionation apportionment. 2. Material and methods 2.1. Study area RDS samples were obtained from three impervious urban surfaces of traffic roads with bituminous pavement (asphalt) in the city of Dresden (51°02′55″ N, 13°44′29″ E), Saxony, in Germany. Three sampling sites, i.e. a city commercial centre (Site W), a city border (Site B) and a rural residential area (Site N), were selected for RDS collection. More details of sampling sites are given in Supplementary Data (Figure S1 and Table S1). 2.2. Sample collection The sampling collection was performed in July 2012. Wet and dry vacuuming methods are the prevailing method of RDS collection (Amato et al., 2010). The vacuum sweep with a water filtration system, also referred to as the wet vacuuming method, has been proven to be more efficient than the dry vacuuming method in removing the fine materials within an impervious surface (Amato et al., 2010; Egodawatta et al., 2013; Gunawardana et al., 2014). Accordingly, a professional vacuum sweeper (Puzzi 100 Super, Kärcher) was employed for sample collection. The vacuum sweeper consisted of an extraction pipe with a polyethylene brush (length, 230 mm) and a water spray (spray pressure, 1 bar) at the top of the vacuum nozzle. The delivered pressure was enough to dislodge fine sediment particles without destroying the road surface. The power requirement was provided by a generator (Honda EU30i, rated power COP, 2.6 kW). The majority of the sediments were assembled near roadside kerbs (Sartor and Boyd, 1972). Therefore, all the enclosed sampling plots were situated next to these areas. In each land use, six sampling plots in close proximity were predefined. The RDS build-up process in each plot started at the same time, after the plot surfaces had been cleaned by the last substantial rainfall event-induced surface runoff. After the stipulated ADPs, i.e. the first, second, third, fourth, sixth, and eighth days since the last substantial rainfall event, the RDS samples were obtained from the respective sampling plots. Every effort was made to ensure consistent initial conditions for each RDS collection. 2.3. Sample fraction The obtained bulk RDS samples were fractionated into sub-samples and wet-sieved with stainless steel sieves (200 × 50 mm, Retsch, Germany) with mesh sizes of 1000 μm, 400 μm, 100 μm and 63 μm in sequence. The particles passing through the 63 μm sieve were filtered by a 0.45 μm cellulose nitrate membrane filter (Sartorius, Germany). According to the German Norm DIN EN ISO 14688-1, sediments with a particle size fraction of 1000–63 μm are classified as sand, 63 μm being classified as the boundary diameter between sand and silt. A particle size

J. Zhang et al. / Science of the Total Environment 532 (2015) 723–732

diameter of 0.45 μm is regarded as the boundary between soluble and particulate matter. Consequently, the size fraction ranges investigated in this study were: 1000–400 μm (course sand), 400–100 μm (medium sand), 100–63 μm (fine sand) and 63–0.45 μm (silt and clay).

2.4. Chemical analysis A total of 68 size-fractionated RDS samples were tested for Cu (copper), Zn (zinc) and Cd (cadmium). The contents of metals were determined according to both the three-step sequential extraction protocol proposed by the Standards, Measurements and Testing Programme (SM&T, formerly Community Bureau of Reference) of the European Commission which has proved reproducible and gave good recoveries with respect to acid dissolution (Davidson et al., 1998) and a pseudototal digestion protocol following the German Norm DIN 38406-8 (E8), DIN 38406-7 (E7), and DIN EN ISO 5961. The three-step sequential extraction procedure aims to fractionate metals in sequence into the operationally defined phases of exchangeable, reducible, oxidizable, and residual fractions. In general, the mobility and bioavailability of metals decrease approximately in the order of sequential extracted chemical fractionation (Davidson et al., 1998). Metals in unstable components of exchangeable and reducible fractions are highly correlated with pollution and pose a greater risk of bioavailability than those in stable components of oxidizable and residual fractions. For example, the residual fraction of metals cannot be taken up by organisms, whereas the exchangeable fraction is the most bioavailable content of all fractions (Sutherland et al., 2012). According to Quevauviller et al. (1997), the chemical extraction reagents and experimental protocol are given in Table 1. More details can be found elsewhere (Quevauviller et al., 1997; Li et al., 2013). The metal contents of each fraction were determined in sequence by an atomic absorption spectrophotometer equipped with a graphite furnace atomization (Varian, SpectrAA 220 Fast Sequential AAS-220 Z). The samples were processed in triplicate throughout the analyses and the results reported were the average values. To maintain the laboratory quality assurance/quality control, Cu (1.014 g/cm3, 20 °C), Zn (1.02 g/cm3, 20 °C), Cd (1.013 g/cm3, 20 °C) and multi-element standard solutions (23 elements in diluted nitric acid, 1.09 g/cm3, 20 °C) for each sample at 1000 mg/L were prepared (Merck KGaA, Darmstadt, Germany). A certified reference material of dry sewage sludge (KS 2/2006) from a European Union reference laboratory (Saechsische Landesanstalt fuer Landwirtschaft. Fachbereich 8 — Landwirtschaftliches Untersuchungswesen, Leipzig, Germany) was used. The recovery (the summation of the four fractions to the pseudo-total metals concentration) ranged from 98–116% for Cu, 109–124% for Zn, and 92–115% for Cd. The limit of quantitation was 1.0 μg/L for Cu, 20 μg/L for Zn, and 0.1 μg/L for Cd. The limit of detection was 0.5 μg/L for Cu, 10 μg/L for Zn, and 0.05 μg/L for Cd.

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2.5. Data analysis Hierarchical cluster analysis was employed for data, which is mainly concerned with identifying the hidden patterns and interrelationships in a data matrix (Sundaray et al., 2011; Acosta et al., 2014). Hierarchical cluster analysis was carried out with OriginPro 9.0 data analysis software. For each metal, the data matrix consisted of 68 objects (size-fractionated RDS samples) and 7 variables (size fraction, ADP, land use, and four-chemical fractionation). The correlation distance and group average cluster method were used to derive the classification. The most similar objects were first grouped, and these initial groups merged according to their similarities. As the similarity decreased, eventually all sub-groups were fused into a single cluster. 3. Results and discussion 3.1. Heavy metal pollution level assessment 3.1.1. Heavy metal solid-phase concentration of bulk RDS The solid-phase concentrations of metals in bulk RDS are given in Table 2. ‘Bulk’ reflects the general concentration for a summation of variously sized fractions and was calculated as follows:

C Bulk ¼

m X C S F; j  M S F; j

ð1Þ

j¼1

where CBulk is the solid-phase concentration of the given bulk metal; CSF,j the solid-phase concentration of the jth size fraction; MSF,j the mass percentage of the jth individual size fraction. As shown in Table 2, it is clear that the highest mean solid-phase concentrations of Cu and Zn were found for a federal highway of site B. The highest mean values of Cd occurred at a city border (or rural area) of site N. However, the lowest mean values of Cu and Zn were found at site N. Comparable with the worldwide data shown in Table S2, the mean solid-phase concentrations of Cu = 150 ± 32 (mean ± standard error of the mean) mg/kg and Zn = 310 ± 32 mg/kg were comparable or lower than the most similar studies reporting ranges from 470 mg/kg (Charlesworth et al., 2003) to 42 mg/kg (Ferreira-Baptista and De Miguel, 2005) for Cu, and 730 mg/kg (Shi et al., 2008) to 110 mg/kg (Kim et al., 1998) for Zn. The mean values of Cd were lower than those in the other studies ranging from 5.0 mg/kg (Faiz et al., 2009) to 1.2 mg/kg (Wei et al., 2009). Furthermore, the Cu and Zn mean solid-phase concentrations in this study were higher than the data from urban and rural soils (Table S2). However, Cd mean solid-phase concentrations were not higher than the data from neighbouring soils. In addition, all the metals' solidphase concentrations were considerably higher than the values for the

Table 1 Three-step sequential extraction (SM&T) and pseudo-total digestion protocols. Step Fraction F1

Exchangeable Exchangeable, carbonate bound

F2

Reducible

F3

Oxidisable

F4

Residuala

a

Nominal target HMs

Chemical reagents and experimental protocols A total of 40 mL of 0.11 mol/L of acetic acid (CH3COOH, pH 2.85) was added to 1.0 g dry weight of RDS samples and shaken for 16

h. The mixture was centrifuged to separate the extract from the residue at 3000 rpm for 20 min. Iron, manganese oxide A total of 40 mL of 0.1 mol/L of hydroxylammonium chloride (NH2OH·HCl, pH 1.5) was added to the residue from step 1 and the extraction performed as above. bound Organically, sulphide A total of 10 mL of hydrogen peroxide (H2O2, 30%) was added to the residue from step 2 at room temperature for 1 h, and then evaporated at 85 ± 2 °C to dryness. Additional 50 mL of 1 mol/L of ammonium acetate (NH4CH3COO, pH 2) was added and bound shaken for 16 h. The extraction performed as above. Remaining, With the microwave assistance, the remaining material after step 3 was digested with 5 mL of nitric acid (HNO3), 5 mL of non-silicate bound ultrapure water and 1 mL of hydrogen peroxide (H2O2, 30%). The digested samples were filtered through the Cellulose Nitrate membrane filter into a 50 mL Erlenmeyer flask and the leftover particles were removed.

Digestion of the residual material is not a speciation of the SM&T three-step sequential extraction protocol, and is referred to as pseudo-total digestion.

J. Zhang et al. / Science of the Total Environment 532 (2015) 723–732

Table 2 Descriptive statistic of metal solid-phase concentrations (in mg/kg of dry mass) in bulk and size dependent RDS. Study area

Commercial W Bulk 1000–400 μm 400–100 μm 100–63 μm 63–0.45 μm Highway B Bulk 1000–400 μm 400–100 μm 100–63 μm 63–0.45 μm Rural N Bulk 1000–400 μm 400–100 μm 100–63 μm 63–0.45 μm Overallc Bulk 1000–400 μm 400–100 μm 100–63 μm 63–0.45 μm

Number of observations

Zn Mean

SDa

Mean

Cu

–b 6 6 6 6

360 120 330 540 760

42 34 64 75 170

170 56 180 310 300

– 6 6 6 6

460 260 500 670 590

180 160 140 270 310

230 240 210 240 220

– 5 5 5 5

99 40 82 120 150

15 12 13 10 23

37 14 47 48 45

– 17 17 17 17

310 140 300 440 500

79 69 71 120 170

150 100 140 200 190

Cd SD

Mean

SD

33 38 28 60 55

0.21 0.068 0.15 0.41 0.49

0.073 0.028 0.040 0.43 0.12

190 300 140 110 140

0.19 0.069 0.16 0.27 0.33

0.038 0.017 0.055 0.086 0.13

0.32 0.099 0.24 0.44 0.55

0.10 0.035 0.053 0.12 0.24

0.24 0.079 0.18 0.37 0.46

0.07 0.027 0.049 0.21 0.16

13 3.5 29 16 11 78 110 64 63 69

a

SD: Standard deviation. ‘Bulk’ reflects the general concentration for a summation of variously sized fractions and was calculated as Eq. (1). c Overall: It indicates the contents regardless of land use (from all the sampling sites). The number of observations for ‘Overall’ is the sum of numbers overserved at all the sampling sites. b

Earth's crust, which indicates a strong influence of anthropogenic activities. In terms of land use, metal contents in RDS samples were compared with the precautionary values of metal contents in soil samples PCSoil, which indicates the potential risk to human health and safeguarding agroforestry production (Shi et al., 2010). As shown in Table S2, the mean values of Cu and Zn in sites W and B were higher than the precautionary values of PCSoil (Cu = 100 mg/kg and Zn = 300 mg/kg). However, the mean solid-phase concentrations of Cd from each site were lower than the precautionary values of PCSoil (Cd = 0.6 mg/g). The results suggest that metals in sites W and B posed potential risks to the human health. Sites W and B were not arable areas for safeguarding agroforestry production. 3.1.2. Heavy metal solid-phase concentration of size-dependent RDS As shown in Table 2, it is evident that coarse size fractions have lower values than fine size fractions. The lowest solid-phase concentrations of Cu, Zn, and Cd were consistently found in 1000–400 size fractions for each land use, which were lower than the precautionary values of metal contents in soil samples (PCSoil in Table S2). General increases in Zn and Cd values were observed with decreasing size fractions. The overall highest solid-phase concentrations of Zn and Cd were found in 63–0.45 μm size fraction, which were higher than the precautionary values of PCSoil. The highest solid-phase concentrations of Cu were frequently found in 100–63 μm size fractions, which were higher than the precautionary value of PCSoil. Therefore, concerning human health and safeguarding agroforestry production, more attention should be paid to the finer RDS and its adsorbed metals.

0.42 mg/m2, and Cd = 1.6 ± 0.27 μg/m2. The high surface load at site W was consistent with the findings of Lau and Stenstrom (2005) that the commercial area had higher metal surface loads than most of the other land uses. A plausible explanation could be the prevailing anthropogenic and traffic activities at site W. In contrast, mean surface loads were very low in the rural area of site N (Cu = 0.15 ± 0.030 mg/m2 and Zn = 0.39 ± 0.050 mg/m2) which was only slightly influenced by traffic or anthropogenic. 3.1.4. Heavy metal surface load of size-dependent RDS Generally, increases in metal surface loads were observed with decreasing size fractions. As shown in Fig. 1(a), the highest Cu mean surface load was found in 400–100 μm, followed by 63–0.45 μm. The mean percentage of size fraction in proportion to the total surface load (a summation of surface loads in individual size fractions) was evenly distributed as 400–100 μm (36%) N 63–0.45 μm (32%) N1000–400 μm (22%) N 100–63 μm (12%). As shown in Fig. 1(b), Zn mean surface loads dominated in the size fractions of 63–0.45 μm and 400–100 μm. The mean percentage of size fraction in proportion to the total surface load was 63–0.45 μm (40%) N 400–100 μm (35%) N 1000–400 μm (14%) N 100–63 μm (11%). The mean surface load of Cd dominated in the size fraction of 63–0.45 μm and exhibited clearer distribution patterns, as shown in Fig. 1(c). The mean percentage of size fraction in proportion to the total surface load was 63–0.45 μm (53%) N 100–63 μm (21%) N 400–100 μm (18%) N 1000–400 μm (8.4%). Accordingly, the particle size distribution results indicate that the RDS with fine size fractions contributed considerably to the total surface load. 3.2. Heavy metal chemical fractionation 3.2.1. Heavy metal chemical fractionation in bulk RDS The proportions of Cu generally increased in the order of sequentially extracted chemical fractionation. The proportion of Cu existed predominantly in the stable components of residual and oxidizable fractions. As shown in Fig. 2(a), the overall proportional order of the four fractions decreased as residual (44 ± 4.2%) N oxidizable (41 ± 3.0%) N exchangeable (12 ± 1.4%) N reducible (3.3 ± 0.32%). The dominant Cu contents in residual fractions indicate that Cu contents in RDS

63-0.45

(a)

100-63 400-100

Size fraction (µm)

726

1000-400

Cu mg/m2

63-0.45

(b)

100-63 400-100 1000-400

Zn mg/m2

63-0.45

(c)

100-63 400-100

Cd µg/m 2

1000-400

3.1.3. Heavy metal surface load of bulk RDS The mean values of overall total surface load were: Cu = 0.85 ± 0.19 mg/m2, Zn = 1.7 ± 0.31 mg/m2, and Cd = 1.2 ± 0.14 μg/m2. With regard to land use, the city commercial centre of site W had the highest mean surface loads of Cu = 1.4 ± 0.24 mg/m2, Zn = 2.9 ±

0.0

0.3

0.6

0.9

Surface load Fig. 1. Patterns of HM surface load of size dependent RDS: (a) Cu; (b) Zn; and (c) Cd.

J. Zhang et al. / Science of the Total Environment 532 (2015) 723–732

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0.8 (a) Overall

(b) W

0.6

Fraction (%)

0.4 0.2 0.0 Exchangeable Reducible Oxidisable Residual

(c) B 0.6

(d) N

0.4

0.2

0.0 Cu

Zn

Cd

Cu

Zn

Cd

Species Fig. 2. Patterns of HM chemical fractionation in bulk RDS samples: (a) Overall; (b) W site; (c) B site; and (d) N site.

were relatively stable and would not show significant transformation under different conditions when discharged into receiving waters by stormwater runoff. Cu contents in oxidizable fractions may be associated with various forms of organic material. The high affinity of Cu to organic matters should due to the fact that Cu can easily complex with organic matter to form the high stability constants of organic-Cu compounds (Sundaray et al., 2011; Li et al., 2013). These compounds could pose a comparably low ecological risk to receiving waters, but would exist for longer periods. Zn had special affinity towards exchangeable and residual fractions. As shown in Fig. 2(a), the exchangeable fraction accounted for 49 ± 2.3% of the total Zn content, and followed by the fraction order of residual (33 ± 3.0%) N oxidizable (9.2 ± 1.0%) N reducible (9.2 ± 0.51%). Metal contents determined by exchangeable extraction are considered to provide a reasonable approximation of the easily mobilized and biologically available content. The higher the proportion of metals in this fraction, the more mobile they are, and the higher the risk to the receiving aquatic environment (Sutherland et al., 2012; Acosta et al., 2014). Therefore, the dominant Zn contents in the exchangeable fraction indicated that Zn contents in RDS could pose a considerably high ecological risk to aquatic biota when discharged into receiving waters. The results were consistent with previous findings in literature that Zn would be one of the most mobile and bioavailable of the anthropogenically impacted elements (Taylor and Owens, 2009; Sutherland et al., 2012; Loganathan et al., 2013). The Cd contents were characteristically enriched in the exchangeable fraction and then significantly decreased in the order of sequentially extracted chemical fractionation. The mean proportional order of the four fractions shown in Fig. 2(a) were generally decreased as exchangeable (59 ± 1.1%) N reducible (19 ± 1.2%) N oxidizable (17 ± 1.4%) N residual (4.4 ± 0.50%). The dominant contents in the exchangeable fraction suggested that Cd is one of the most susceptible metals to mobilization during stormwater runoff (Taylor and Owens, 2009).

In terms of individual land use, according to the data summarized in Fig. 2(b)–(d), the chemical fractionation proportional patterns of given metals were relatively similar. Hence, land use had an insignificant influence on the apportioning of metals' chemical fractionation. However, the highest exchangeable fraction of Zn was found at site B. The highest exchangeable fraction of Cd was found at site N. Therefore, both sites B and N posed significant risks to stormwater runoff because of the high proportions of metal unstable exchangeable fractions. 3.2.2. Heavy metal chemical fractionation of size-dependent RDS To determine the mass percentage of chemical fractionation in sizedependent RDS, the modified form of size fraction loading P presented in Sutherland et al. (2012) was applied in this study: C i; j  Mi  100% P ¼ Xm C  Mi j¼1 i; j

ð2Þ

where Ci is the surface load of the jth metal in the ith size fraction in one of the four sequentially extracted chemical fractionation; Mi is the dry mass percentage of the ith size fraction; and m is the number of given metal. Therefore, for a given metal, the summation of the four chemical fractionation in each size fraction = 100%. Regardless of land use, Cu size-dependent surface loads in exchangeable, reducible and oxidizable fractions generally increased with decreasing size fractions (Fig. 3). The exception was the size fraction of 100–63 μm due to the comparably shorter size fraction range. All the size fractions contributed inconsiderably to the unstable components of exchangeable and reducible fractions (ranging from 6.5 to 0.20%). Because of the considerabe contributions from the coarse size fractions, Cu surface loads in residual fractions were dominated, which were averagely attributed 15% to 100–400 μm and 9.4% to 400–1000 μm size fractions. The finest fraction of 63–0.45 μm contributed averagely 13% to

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W site

Reducible Exchangeable

63-0.45

N site

B site Cu

100-63 400-100 1000-400

100-63 400-100 1000-400 63-0.45

Oxidisable

Size fraction (µm)

63-0.45

100-63 400-100 1000-400

Residual

63-0.45 100-63 400-100 1000-400 1E-3

0.01

0.1

1E-3

0.01

0.1

1E-3

0.01

0.1

1

Cu mean loading (100%) Fig. 3. Loads of Cu chemical fractionation in size dependent RDS.

residual fraction. Therefore, a considerable proportion of Cu in stormwater runoff could be more easily controlled and mitigated by removing coarse RDS from impervious surfaces. As shown in Fig. 4, except for the size fraction of 100–63 μm, Zn surface loads in individual chemical fractionation increased with decreasing size fractions. Surface load in 63–0.45 μm dominated all chemical fractionation, especially the exchangeable fraction with an average contribution of 23%. It is notable because this is the size fraction that would be mostly widely digested and assimilated by aquatic biota, especially

B site

100-63

N site Reducible Exchangeable

W site 63-0.45

benthic feeders (Sutherland et al., 2012). The residual fractions were the next largest chemical fractionation for Zn with a dominant contribution (14%) of 63–0.45 μm. Accordingly, the mitigation strategy for Zn should be capable of trapping RDS with the size fraction of 63–0.45 μm. As shown in Fig. 5, Cd size-dependent surface loads in exchangeable, reducible and oxidizable fractions generally increased with decreasing size fractions. Most individual size fractions contributed more equally to Cd residual fractionation. Exchangeable fraction was the dominant Cd component with a considerable contribution of 63–0.45 μm (34%).

Zn

400-100 1000-400

100-63 400-100 1000-400

Oxidisable

63-0.45 100-63 400-100 1000-400 63-0.45

Residual

Size fraction (µm)

63-0.45

100-63 400-100 1000-400 1E-3

0.01

0.1

1E-3

0.01

0.1

1E-3

Zn mean loading (100%) Fig. 4. Loads of Zn chemical fractionation in size dependent RDS.

0.01

0.1

1

J. Zhang et al. / Science of the Total Environment 532 (2015) 723–732

B site

N site Reducible Exchangeable

W site 63-0.45

729

Cd

100-63 400-100 1000-400

100-63 400-100 1000-400 63-0.45

Oxidisable

Size fraction (µm)

63-0.45

100-63 400-100 1000-400

Residual

63-0.45 100-63 400-100 1000-400 1E-3

0.01

1E-3

0.1

0.01

0.1

1E-3

0.01

0.1

1

Cd mean loading (100%) Fig. 5. Loads of Cd chemical fractionation in size dependent RDS.

Surface load (µg/m 2) Surface load (mg/m2) Surface load (mg/m2)

The finest size fraction of 63–0.45 μm was also the most significant contributor to the Cd components in reducible and oxidizable fractions, accounting for 10% and 8.7%, respectively. Therefore, the management of the fine size fraction is a key issue for the potential mitigation of Cd in stormwater runoff to remove RDS from impervious surfaces.

3.3.1. Heavy metal build-up dynamic of bulk RDS As given in Fig. 6, generally, increasing tendencies of Cu and Zn total surface loads (a summation of surface loads in individual chemical

W site

2.40 1.92

3.3. Heavy metal build-up dynamic

B site

N site

Cu

Cu

Cu

Zn

Zn

Zn

Cd

Cd

Cd

Residual Oxidisable Reducible Exchangeable

1.44 0.96 0.48 0.00 3.80 2.85 1.90 0.95 0.00 2.44 1.83 1.22 0.61 0.00 1

2

3

4

5

6

7

8

1

2

3

4

5

6

7

8

1

2

3

Antecedent dry weather period (d) Fig. 6. Build-up patterns of Cu, Zn and Cd in bulk RDS.

4

5

6

7

8

730

J. Zhang et al. / Science of the Total Environment 532 (2015) 723–732

0.76

400 - 100 µm

1000 - 400 µm

0.95

100 - 63 µm

63 - 0.45 µm

W

W

W

W

B

B

B

B

N

N

N

1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

Cu surface load (mg/m2)

0.57 0.38 0.19 0.00 1.25 1.00 0.75 0.50 0.25 0.00 0.12

N

0.09 0.06

Residual Oxidisable Reducible Exchangeable

0.03 0.00 1 2 3 4 5 6 7 8

Antecedent dry weather period (d) Fig. 7. Build-up patterns of Cu in size fractionated RDS.

fractionation) were detected at sites W and B with an increasing duration of ADP. However, the clear increase in total surface load of Cd was only found at site B. ADP had a minor influence on metals contents at site N where relatively stable total surface loads were found during the sampling period. Regarding chemical fractionation, Cu stable components of residual and oxidizable fractions increased with an increasing duration of ADP at sites W and B. This result indicates that although the total surface

1000 - 400 µm 2.0

Zn surface load (mg/m2)

1.5

load of Cu increased, the potential risk of Cu was not evidently increased over ADP. This is due to that the increasing total surface load was dominantly contributed by stable components which posed low risks of bioavailability. The increasing total surface load of Zn was mainly attributed to the increasing components of exchangeable and residual fractions. Because of the readily bioavailability of exchangeable fractions, the potential risk of Zn increased during ADP at sites W and B. A comparably clear build-up pattern of Cd was found at site B, and was

400 - 100 µm

100 - 63 µm

63 - 0.45 µm

w

w

w

w

B

B

B

B

N

N

N

1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

1.0 0.5 0.0 0.9 0.6 0.3 0.0 0.3 0.2

N

Residual Oxidisable Reducible Exchangeable

0.1 0.0

1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

Antecedent dry weather period (d) Fig. 8. Build-up patterns of Zn in size fractionated RDS.

J. Zhang et al. / Science of the Total Environment 532 (2015) 723–732

400 - 100 µm

1000 - 400 µm

731

100 - 63 µm

63 - 0.45 µm

1.30 1.04

W

W

W

W

B

B

B

B

N

N

1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

Cd surface load (µg/m 2)

0.78 0.52 0.26 0.00 0.39 0.26 0.13 0.00 0.75

Residual Oxidisable Reducible Acid soluble

N

0.50

N

0.25 0.00 1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8

Antecedent dry weather period (d) Fig. 9. Build-up patterns of Cd in size fractionated RDS.

considerably attributed to the exchangeable fraction. In other words, the potential risk of Cd increased during ADP at site B. Therefore, to prevent urban runoff pollution, site-specific surface sweeping performances with different time intervals should be designed for individual metals and individual land-use types. Special concern should be paid to Zn and Cd, which could pose higher risks with a longer ADP.

especially in fine fractions. Cd exhibited a more complex pattern, as shown in Fig. 9. Comparably increasing tendencies occurred at site B for each size fraction. Stable patterns of surface loads were found at site N and especially in the fine size fractions. At the commercial centre of site W, more clear build-up tendencies were found in coarse size fractions than fine size fractions.

3.3.2. Heavy metal build-up dynamic of size-dependent RDS As shown in Fig. 7, Cu surface loads generally increased with longer ADPs for most of the size fractions at each site. More clear build-up tendencies were found in coarse size fractions (1000–400 and 400–100 μm) rather than fine size fractions (100–63 and 63–0.45 μm), and especially for oxidizable and residual fractions. In terms of land use, more evident build-up patterns were found at sites B and W in each size fraction. As shown in Fig. 8, generally, Zn contents in the exchangeable fractions increased over the increasing ADP for most of the size fractions at each sampling site. Therefore, attention should be paid to the increasing Zn component in exchangeable fractions,

3.4. Hierarchical cluster analysis Visible dendrogram classification was derived by hierarchical cluster analysis and is shown in Fig. 10. The inherent correlations in particle size distribution, land use, ADP and chemical fractionation varied for individual metals, largely because of the different sources of metals and the RDS properties which affect mobilization–immobilization processes of metals. As regards Cu, ADP had a strong influence on Cu contents in the exchangeable, oxidizable and reducible fractions. The reducible fraction was relatively dependent on the size fraction. However, compared

1.5

Euclidian distance

Cu

Zn

Cd

1.0

0.5

0.0 e Siz

ct fra

l P al le le le se on able cible able ADP dual use on ion ADP eable sable cible idua d use cti cti AD geab ducib disab esidu nd u s d si a fra fra hange Redu xidi i n g x i d i R e d u R e s Lan n Re R Re Lan a a x L e e h h z z O O O c c Si Si Exc Ex Ex Fig. 10. Dendrogram of hierarchical cluster analysis of variables.

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with ADP and size fraction, land use had a minor influence on the apportioning of chemical fractionation. In terms of Zn, the most important influencing factor on four-chemical fractionation was ADP. Fourchemical fractionation was strongly size dependent. However, land use had an insignificant influence on the portioning of Zn chemical fractionation. The exchangeable, reducible and oxidizable fractions of Cd were highly dependent on the size fraction. ADP only had a strong influence on Cd contents in the residual fractions. However, land use had a minor influence on the apportioning of Cd chemical fractionation. 4. Conclusions Solid-phase concentration (mg/kg), surface load (mg/m2), and chemical fractionation of Cu, Zn and Cd were determined in this study. Results indicate that RDS (road-deposited sediment) and its adsorbed heavy metals could pose higher risks to aquatic biota after a longer ADP (dry-weather period). Great concern should be addressed to the finer RDS-adsorbed metals which are more likely to reach receiving water. The results of hierarchical cluster analysis indicate that the duration of ADP significantly affected the build-up processes of metals. The metals' chemical fractionation were very dependent on the particle size distributions. The land use showed a minor or negligible influence on the apportioning of the metals' chemical fractionation. The data reported herein could assist potential stormwater pollution management which is designed to remove hazardous materials from impervious urban surfaces by trapping RDS with fine size fractions, and could be used for metal source-oriented mitigation of rainfallinduced urban runoff pollution. Further work should be conducted to define the standard methods of the sample collection and fractionation, and offer guidelines on evaluation of the metal pollution level in the field of RDS. Acknowledgement The authors would like to gratefully thank Mr Gregor Pfalz for his contribution to the sample collection. Thanks to Mrs Dr Heike Brückner, Mrs Ulrike Gebauer, Mrs Sinaida Heidt and Miss Yuan Wang for their assistance with laboratory analysis. The authors also gratefully acknowledge the state-sponsored scholarship programme provided by China Scholarship Council (CSC) (No. 2010605020 and No. 2010605021), and Completion Grant (PSP-Element: F-003661-553-52A-2330000) provided by the Graduate Academy of the Technische Universität Dresden by means of the Excellence Initiative by the German Federal and State Governments. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2015.06.074. References Acosta, J.A., Faz, A., Kalbitz, K., Jansen, B., Martinez-Martinez, S., 2014. Partitioning of heavy metals over different chemical fraction in street dust of Murcia (Spain) as a basis for risk assessment. J. Geochem. Explor. 144, 298–305. Amato, F., Querol, X., Johansson, C., Nagl, C., Alastuey, A., 2010. A review on the effectiveness of street sweeping, washing and dust suppressants as urban PM control methods. Sci. Total Environ. 408 (16), 3070–3084. Bradl, H.B., 2004. Adsorption of heavy metal ions on soils and soils constituents. J. Colloid Interface Sci. 277 (1), 1–18. Bridge, J.S., 2009. Rivers and Floodplains: Forms, Processes, and Sedimentary Record. John Wiley & Sons. Charlesworth, S., Everett, M., McCarthy, R., Ordonez, A., de Miguel, E., 2003. A comparative study of heavy metal concentration and distribution in deposited street dusts in a large and a small urban area: Birmingham and Coventry, West Midlands, UK. Environ. Int. 29 (5), 563–573. Davidson, C.M., Duncan, A.L., Littlejohn, D., Ure, A.M., Garden, L.M., 1998. A critical evaluation of the three-stage BCR sequential extraction procedure to assess the potential

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